prompt_trendyol_v2.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:18:46.644068Z","iopub.status.busy":"2024-05-30T19:18:46.643818Z","iopub.status.idle":"2024-05-30T19:18:47.450532Z","shell.execute_reply":"2024-05-30T19:18:47.449532Z","shell.execute_reply.started":"2024-05-30T19:18:46.644045Z"},"executionInfo":{"elapsed":24027,"status":"ok","timestamp":1724350180281,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"WZoyywJ35xQQ","outputId":"393a6f8c-5f46-40b1-b84c-c73baeabda03"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:18:47.453003Z","iopub.status.busy":"2024-05-30T19:18:47.452302Z","iopub.status.idle":"2024-05-30T19:19:05.486916Z","shell.execute_reply":"2024-05-30T19:19:05.485937Z","shell.execute_reply.started":"2024-05-30T19:18:47.452967Z"},"executionInfo":{"elapsed":16550,"status":"ok","timestamp":1724350196828,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"wOylb_jIFUAi","outputId":"a423c8d0-eeda-4ad2-a14c-dff89043a090"},"outputs":[{"name":"stdout","output_type":"stream","text":["NLTK Downloader\n","---------------------------------------------------------------------------\n","    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n","---------------------------------------------------------------------------\n","Downloader> d\n","\n","Download which package (l=list; x=cancel)?\n","  Identifier> punkt\n"]},{"output_type":"stream","name":"stderr","text":["    Downloading package punkt to /root/nltk_data...\n","      Unzipping tokenizers/punkt.zip.\n"]},{"name":"stdout","output_type":"stream","text":["\n","---------------------------------------------------------------------------\n","    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n","---------------------------------------------------------------------------\n","Downloader> d\n","\n","Download which package (l=list; x=cancel)?\n","  Identifier> stopwords\n"]},{"output_type":"stream","name":"stderr","text":["    Downloading package stopwords to /root/nltk_data...\n","      Unzipping corpora/stopwords.zip.\n"]},{"name":"stdout","output_type":"stream","text":["\n","---------------------------------------------------------------------------\n","    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n","---------------------------------------------------------------------------\n","Downloader> q\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":2}],"source":["import nltk\n","nltk.download() #punkt stopwords"]},{"cell_type":"markdown","metadata":{"id":"XY0kKs5VKAwG"},"source":["## Installations and imports"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:19:07.843266Z","iopub.status.busy":"2024-05-30T19:19:07.842927Z","iopub.status.idle":"2024-05-30T19:20:50.023801Z","shell.execute_reply":"2024-05-30T19:20:50.022718Z","shell.execute_reply.started":"2024-05-30T19:19:07.843240Z"},"executionInfo":{"elapsed":174587,"status":"ok","timestamp":1724350373213,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"hwbiiBi0FUC3","outputId":"549ca550-a153-47a4-f65b-8f41a6c87bd5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting torch==2.1.2\n","  Downloading torch-2.1.2-cp310-cp310-manylinux1_x86_64.whl.metadata (25 kB)\n","Requirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (2.17.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.1.2) (3.15.4)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.1.2) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.1.2) (1.13.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.1.2) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.1.2) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch==2.1.2) (2024.6.1)\n","Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch==2.1.2)\n","  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch==2.1.2)\n","  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch==2.1.2)\n","  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch==2.1.2)\n","  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu12==12.1.3.1 (from torch==2.1.2)\n","  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.0.2.54 (from torch==2.1.2)\n","  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-curand-cu12==10.3.2.106 (from torch==2.1.2)\n","  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch==2.1.2)\n","  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch==2.1.2)\n","  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-nccl-cu12==2.18.1 (from torch==2.1.2)\n","  Downloading nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl.metadata (1.8 kB)\n","Collecting nvidia-nvtx-cu12==12.1.105 (from torch==2.1.2)\n","  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n","Collecting triton==2.1.0 (from torch==2.1.2)\n","  Downloading triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)\n","Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch==2.1.2)\n","  Using cached nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.4.0)\n","Requirement already satisfied: grpcio>=1.48.2 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.64.1)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (3.7)\n","Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.26.4)\n","Requirement already satisfied: protobuf!=4.24.0,<5.0.0,>=3.19.6 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (3.20.3)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages 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uninstall: torch\n","    Found existing installation: torch 2.3.1+cu121\n","    Uninstalling torch-2.3.1+cu121:\n","      Successfully uninstalled torch-2.3.1+cu121\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","torchaudio 2.3.1+cu121 requires torch==2.3.1, but you have torch 2.1.2 which is incompatible.\n","torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.1.2 which is incompatible.\n","torchvision 0.18.1+cu121 requires torch==2.3.1, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 torch-2.1.2 triton-2.1.0\n","Collecting transformers==4.36.2\n","  Downloading transformers-4.36.2-py3-none-any.whl.metadata (126 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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requests->transformers==4.36.2) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.36.2) (2024.7.4)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.26.1) (1.13.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.26.1) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.26.1) (3.1.4)\n","Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.105)\n","Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.105)\n","Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in 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This could take a while.\n","Collecting multiprocess (from datasets==2.16.1)\n","  Downloading multiprocess-0.70.15-py310-none-any.whl.metadata (7.2 kB)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets==2.16.1) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets==2.16.1) (2024.1)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets==2.16.1) (2024.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets==2.16.1) (1.16.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate==0.26.1) (2.1.5)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate==0.26.1) (1.3.0)\n","Downloading transformers-4.36.2-py3-none-any.whl (8.2 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m78.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading datasets-2.16.1-py3-none-any.whl (507 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m36.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading accelerate-0.26.1-py3-none-any.whl (270 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading bitsandbytes-0.42.0-py3-none-any.whl (105.0 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading dill-0.3.7-py3-none-any.whl (115 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading fsspec-2023.10.0-py3-none-any.whl (166 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.4/166.4 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading responses-0.18.0-py3-none-any.whl (38 kB)\n","Downloading tokenizers-0.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m93.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, responses, multiprocess, bitsandbytes, tokenizers, transformers, datasets, accelerate, evaluate\n","  Attempting uninstall: fsspec\n","    Found existing installation: fsspec 2024.6.1\n","    Uninstalling fsspec-2024.6.1:\n","      Successfully uninstalled fsspec-2024.6.1\n","  Attempting uninstall: tokenizers\n","    Found existing installation: tokenizers 0.19.1\n","    Uninstalling tokenizers-0.19.1:\n","      Successfully uninstalled tokenizers-0.19.1\n","  Attempting uninstall: transformers\n","    Found existing installation: transformers 4.42.4\n","    Uninstalling transformers-4.42.4:\n","      Successfully uninstalled transformers-4.42.4\n","  Attempting uninstall: accelerate\n","    Found existing installation: accelerate 0.32.1\n","    Uninstalling accelerate-0.32.1:\n","      Successfully uninstalled accelerate-0.32.1\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2023.10.0 which is incompatible.\n","torchaudio 2.3.1+cu121 requires torch==2.3.1, but you have torch 2.1.2 which is incompatible.\n","torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.1.2 which is incompatible.\n","torchvision 0.18.1+cu121 requires torch==2.3.1, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed accelerate-0.26.1 bitsandbytes-0.42.0 datasets-2.16.1 dill-0.3.7 evaluate-0.4.1 fsspec-2023.10.0 multiprocess-0.70.15 responses-0.18.0 tokenizers-0.15.2 transformers-4.36.2 xxhash-3.5.0\n","Collecting git+https://github.com/huggingface/trl@a3c5b7178ac4f65569975efadc97db2f3749c65e\n","  Cloning https://github.com/huggingface/trl (to revision a3c5b7178ac4f65569975efadc97db2f3749c65e) to /tmp/pip-req-build-20svkbvu\n","  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/trl /tmp/pip-req-build-20svkbvu\n","  Running command git rev-parse -q --verify 'sha^a3c5b7178ac4f65569975efadc97db2f3749c65e'\n","  Running command git fetch -q https://github.com/huggingface/trl a3c5b7178ac4f65569975efadc97db2f3749c65e\n","  Running command git checkout -q a3c5b7178ac4f65569975efadc97db2f3749c65e\n","  Resolved https://github.com/huggingface/trl to commit a3c5b7178ac4f65569975efadc97db2f3749c65e\n","  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from trl==0.7.11.dev0) (2.1.2)\n","Requirement already satisfied: transformers>=4.31.0 in /usr/local/lib/python3.10/dist-packages (from trl==0.7.11.dev0) (4.36.2)\n","Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.10/dist-packages (from trl==0.7.11.dev0) (1.26.4)\n","Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from trl==0.7.11.dev0) (0.26.1)\n","Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (from trl==0.7.11.dev0) (2.16.1)\n","Collecting tyro>=0.5.11 (from trl==0.7.11.dev0)\n","  Downloading tyro-0.8.8-py3-none-any.whl.metadata (8.4 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (3.15.4)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (1.13.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (2023.10.0)\n","Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n","Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n","Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n","Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (8.9.2.26)\n","Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.3.1)\n","Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (11.0.2.54)\n","Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (10.3.2.106)\n","Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (11.4.5.107)\n","Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.0.106)\n","Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (2.18.1)\n","Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n","Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.7.11.dev0) (2.1.0)\n","Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.4.0->trl==0.7.11.dev0) (12.6.20)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (0.23.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (24.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (2024.5.15)\n","Requirement already satisfied: requests in 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psutil in /usr/local/lib/python3.10/dist-packages (from accelerate->trl==0.7.11.dev0) (5.9.5)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.7.11.dev0) (14.0.2)\n","Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.7.11.dev0) (0.6)\n","Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.7.11.dev0) (0.3.7)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.7.11.dev0) (2.1.4)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.7.11.dev0) (3.5.0)\n","Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.7.11.dev0) (0.70.15)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.7.11.dev0) (3.10.5)\n","Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.7.11.dev0) (2.4.0)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.7.11.dev0) (1.3.1)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.7.11.dev0) (24.2.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.7.11.dev0) (1.4.1)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.7.11.dev0) (6.0.5)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.7.11.dev0) (1.9.4)\n","Requirement already satisfied: async-timeout<5.0,>=4.0 in 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built peft\n","Installing collected packages: peft\n","Successfully installed peft-0.7.2.dev0\n"]}],"source":["# Install Pytorch & other libraries\n","!pip install \"torch==2.1.2\" tensorboard\n","\n","# Install Hugging Face libraries\n","!pip install  --upgrade \\\n","  \"transformers==4.36.2\" \\\n","  \"datasets==2.16.1\" \\\n","  \"accelerate==0.26.1\" \\\n","  \"evaluate==0.4.1\" \\\n","  \"bitsandbytes==0.42.0\" \\\n","  # \"trl==0.7.10\" # \\\n","  # \"peft==0.7.1\" \\\n","\n","# install peft & trl from github\n","!pip install git+https://github.com/huggingface/trl@a3c5b7178ac4f65569975efadc97db2f3749c65e --upgrade\n","!pip install git+https://github.com/huggingface/peft@4a1559582281fc3c9283892caea8ccef1d6f5a4f --upgrade\n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:20:50.026434Z","iopub.status.busy":"2024-05-30T19:20:50.026093Z","iopub.status.idle":"2024-05-30T19:21:08.396080Z","shell.execute_reply":"2024-05-30T19:21:08.395315Z","shell.execute_reply.started":"2024-05-30T19:20:50.026402Z"},"id":"ve64zirMFUFp"},"outputs":[],"source":["# import the required libraries\n","import os\n","import torch\n","import string\n","import pandas as pd\n","import numpy as np\n","from pprint import pprint\n","from random import randrange\n","from functools import partial\n","from tqdm import tqdm\n","\n","from sklearn.model_selection import train_test_split\n","\n","import re\n","\n","from nltk.corpus import stopwords\n","stop_words = set(stopwords.words('turkish'))\n","\n","from torch.utils.data import DataLoader\n","import datasets # !pip install datasets\n","from datasets import load_dataset, Dataset\n","\n","from transformers import AutoConfig\n","from peft import LoraConfig, PeftModel, TaskType\n","\n","# tokenizers\n","from nltk.tokenize import word_tokenize\n","from transformers import (\n","                          GPTJForSequenceClassification,\n","                          AutoModelForSequenceClassification,\n","                          AutoTokenizer,\n","                          Trainer,\n","                          AutoModelForCausalLM,\n","                          BitsAndBytesConfig,\n","                          DataCollatorForLanguageModeling,\n","                          EarlyStoppingCallback,\n","                          TrainingArguments,\n","                          HfArgumentParser,\n","                          pipeline,\n","                          logging,\n","                          set_seed\n","                          )\n","\n","import bitsandbytes as bnb\n","from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, PeftModel, AutoPeftModelForCausalLM\n","from trl import SFTTrainer\n","from trl import setup_chat_format\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.metrics import (accuracy_score,\n","                             f1_score,\n","                             classification_report,\n","                             confusion_matrix)\n","\n","import evaluate\n","from evaluate import load\n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:23.100100Z","iopub.status.busy":"2024-05-30T19:21:23.099743Z","iopub.status.idle":"2024-05-30T19:21:23.194502Z","shell.execute_reply":"2024-05-30T19:21:23.193741Z","shell.execute_reply.started":"2024-05-30T19:21:23.100071Z"},"id":"2PQkstd5FUIR"},"outputs":[],"source":["df_tr_gr_train = pd.read_csv(\"/content/drive/MyDrive/llm_denemeler/Subtask_A/tr_gr_train.csv\")\n","df_refugee_train = pd.read_csv(\"/content/drive/MyDrive/llm_denemeler/Subtask_A/refugee_train.csv\")\n","df_isr_pal_train = pd.read_csv(\"/content/drive/MyDrive/llm_denemeler/Subtask_A/isr_pal_train.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"execution":{"iopub.execute_input":"2024-05-30T19:21:23.338341Z","iopub.status.busy":"2024-05-30T19:21:23.338037Z","iopub.status.idle":"2024-05-30T19:21:23.356883Z","shell.execute_reply":"2024-05-30T19:21:23.356022Z","shell.execute_reply.started":"2024-05-30T19:21:23.338315Z"},"executionInfo":{"elapsed":35,"status":"ok","timestamp":1724350434570,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"-VWQLwHVFVGY","outputId":"38613bbc-b509-4065-b3d2-c8fe5b80f20b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    id                                               text  \\\n","0  1568524118357864451  Yunan ülkemizi işgal etmiş namusumuza tecavüz ...   \n","1  1527761356061134851  @FeramuzKartal @Neslihan662 Düşüncen dogru kon...   \n","2  1503008593372749826  @UfukUras Adaların silahlandırılması savaş seb...   \n","3  1568255642095296513  @___MuraT_ASM___ İzmire gavur diyen keske yuna...   \n","4  1525787544083910656  Bugünden 103 yıl önce İzmir’de Yunan işgal bir...   \n","\n","   label  \n","0      1  \n","1      0  \n","2      0  \n","3      1  \n","4      0  "],"text/html":["\n","  <div id=\"df-7658d39f-7896-42d2-85cd-6ec45fe22b7c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n"," 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\"#\\u0627\\u0644\\u0645\\u0633\\u062c\\u062f_\\u0627\\u0644\\u0623\\u0642\\u0635\\u0649\\n#freepalastine \\n#MescidiAksadaBask\\u0131nVar \\n#MescidiAksaOnurumuzdur \\n#MescidiAksa https://t.co/hEZqudLHbw\",\n          \"6 milyon suriyeliye 6 y\\u0131ld\\u0131r yard\\u0131m edenler, kendi vatanda\\u015f\\u0131na 2 ay bakar\\u0131m diyemiyor aksine para istiyor.#vaka7402 #coronavirus\",\n          \"@@user suriyelilere bakkalda veriliyor pasaportlar ayr\\u0131ca t\\u00fcrkiye cumhuriyeti pasaportunun de\\u011ferini d\\u00fc\\u015f\\u00fcren bu videoyu \\u00e7ekenler de\\u011fil akepe h\\u00fck\\u00fcmetinin ta kendisidir\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":6}],"source":["df_train = pd.concat([df_tr_gr_train, df_isr_pal_train, df_refugee_train], ignore_index=True)\n","df_train.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:21:23.598879Z","iopub.status.busy":"2024-05-30T19:21:23.598597Z","iopub.status.idle":"2024-05-30T19:21:23.606268Z","shell.execute_reply":"2024-05-30T19:21:23.605274Z","shell.execute_reply.started":"2024-05-30T19:21:23.598857Z"},"executionInfo":{"elapsed":32,"status":"ok","timestamp":1724350434570,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"vAOqfYihFVId","outputId":"0c31a724-0b3e-4f9e-81f5-ac448da019b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Yunan ülkemizi işgal etmiş namusumuza tecavüz ediyor niçin onların ağzına sictik denize döktük demiyorsunuz Osmanlıyı kotuluyorsunuz.Dörtyüz sene çok mu gücünüze gitti.Hala acıyormu. 1 \n","\n","@hurmafidesi #KuduesBizimdir \n","#KudusBizimdir 0 \n","\n","Mescidi aksâ yanarken arkada zafer kutlaması yapan yahudiler...\n","ALLAH(c.c)'ın lâneti üzerinize olsun..\n","#Kahrolsunİsrail \n","#israilteroerdevletidir \n","#MescidiAksaOnurumuzdur https://t.co/4epq5GIBaM 1 \n","\n","@username @username Sözcü gazetesi okuyanlar da  vatansever ; kendi vatanlarını seviyorlar ABD iTrail Ermenistan rumkesimi Yunanistan 1 \n","\n","@@user hocam ülkenin huzuru kalmadı bu şekilde giderse 20 30 seneye türk ırkı kendi vatanında sığınmacı durumuna düşecektir.. misafirlik bir yere kadardır misafirin kısa kalanı makbuldür 0 \n","\n"]}],"source":["print(df_train[\"text\"].iloc[0], df_train[\"label\"].iloc[0], \"\\n\")\n","print(df_train[\"text\"].iloc[1000], df_train[\"label\"].iloc[1000], \"\\n\")\n","print(df_train[\"text\"].iloc[2000], df_train[\"label\"].iloc[2000], \"\\n\")\n","print(df_train[\"text\"].iloc[3000], df_train[\"label\"].iloc[3000] , \"\\n\")\n","print(df_train[\"text\"].iloc[4000], df_train[\"label\"].iloc[4000], \"\\n\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:21:23.766812Z","iopub.status.busy":"2024-05-30T19:21:23.766450Z","iopub.status.idle":"2024-05-30T19:21:23.773520Z","shell.execute_reply":"2024-05-30T19:21:23.772578Z","shell.execute_reply.started":"2024-05-30T19:21:23.766786Z"},"executionInfo":{"elapsed":28,"status":"ok","timestamp":1724350434570,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"aWI6VvlcFVLE","outputId":"b19e8cbd-3997-41ef-b1cb-b5c278eeb9fc"},"outputs":[{"output_type":"stream","name":"stdout","text":["#kürecik açıkken kahrolsun israil naraları 😁\n","#FilistinKazanıyor neyi?\n","#FilistinDireniyor kime?\n","#KüreciğiKapat yoo? 0 \n","\n"]}],"source":["# emojiler\n","print(df_train[\"text\"].iloc[1200], df_train[\"label\"].iloc[1200] , \"\\n\")"]},{"cell_type":"markdown","metadata":{"id":"5cb_IT0KJPwk"},"source":["## Process The Data"]},{"cell_type":"markdown","metadata":{"id":"JLtnkYubJQDY"},"source":["**Extending the stopwords**"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:24.284068Z","iopub.status.busy":"2024-05-30T19:21:24.283704Z","iopub.status.idle":"2024-05-30T19:21:24.288465Z","shell.execute_reply":"2024-05-30T19:21:24.287594Z","shell.execute_reply.started":"2024-05-30T19:21:24.284039Z"},"id":"4j6liHAQFVN2"},"outputs":[],"source":["stop_words = stop_words.union([\"bir\", \"kadar\", \"sonra\", \"kere\", \"mi\", \"ye\", \"te\", \"ta\", \"nun\", \"daki\", \"nın\", \"ten\", \"nin\", \"nün\"])\n","#stop_words"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:24.459808Z","iopub.status.busy":"2024-05-30T19:21:24.459441Z","iopub.status.idle":"2024-05-30T19:21:24.464702Z","shell.execute_reply":"2024-05-30T19:21:24.463605Z","shell.execute_reply.started":"2024-05-30T19:21:24.459778Z"},"id":"e9xQ9z2WFVRR"},"outputs":[],"source":["def remove_punctuation(text):\n","    return ''.join(d for d in text if d not in string.punctuation or d == '!' or d == '?')"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:24.637582Z","iopub.status.busy":"2024-05-30T19:21:24.637271Z","iopub.status.idle":"2024-05-30T19:21:24.642159Z","shell.execute_reply":"2024-05-30T19:21:24.641126Z","shell.execute_reply.started":"2024-05-30T19:21:24.637535Z"},"id":"MvC4I0QjJUv7"},"outputs":[],"source":["def remove_extra_space(text):\n","    text = re.sub(' +', ' ', text)\n","    return text.strip()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:24.804503Z","iopub.status.busy":"2024-05-30T19:21:24.803948Z","iopub.status.idle":"2024-05-30T19:21:24.808559Z","shell.execute_reply":"2024-05-30T19:21:24.807565Z","shell.execute_reply.started":"2024-05-30T19:21:24.804476Z"},"id":"ZjNVVUO0JUyN"},"outputs":[],"source":["def remove_emoji(text):\n","    text = re.sub(r'[\\U00010000-\\U0010ffff]', '', text)\n","    return text"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:25.007409Z","iopub.status.busy":"2024-05-30T19:21:25.007147Z","iopub.status.idle":"2024-05-30T19:21:25.014285Z","shell.execute_reply":"2024-05-30T19:21:25.013468Z","shell.execute_reply.started":"2024-05-30T19:21:25.007387Z"},"id":"20iuTh0HJU01"},"outputs":[],"source":["# creating a function to process the data\n","def data_processing(text):\n","\n","    text = re.sub('\\n', '', text)\n","    text = re.sub('<.*?>+', '', text)\n","    text = re.sub(r\"https\\S+|www\\S+http\\S+\", '', text, flags = re.MULTILINE)\n","    text = re.sub(r'\\@w+|\\#', '', text)\n","    text = text.replace('I','ı') # lower() için\n","    text = text.replace('İ','i')\n","    text = text.lower()\n","    text = re.sub(r'\\d', '', text)\n","\n","    text = remove_punctuation(text)\n","    text = remove_extra_space(text)\n","    text = remove_emoji(text)\n","\n","    text_tokens = word_tokenize(text)\n","\n","    filtered_text = [w for w in text_tokens if not w in stop_words]\n","\n","    return \" \".join(filtered_text)\n"]},{"cell_type":"markdown","metadata":{"id":"AP6Gd82pJaVm"},"source":["### Applying the processing algorithm"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:25.426625Z","iopub.status.busy":"2024-05-30T19:21:25.426315Z","iopub.status.idle":"2024-05-30T19:21:25.855581Z","shell.execute_reply":"2024-05-30T19:21:25.854830Z","shell.execute_reply.started":"2024-05-30T19:21:25.426601Z"},"id":"0K6mqJ92JU3T"},"outputs":[],"source":["df_tr_gr_train.text = df_tr_gr_train['text'].apply(data_processing)\n","df_tr_gr_train = df_tr_gr_train.drop_duplicates('text')"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:25.858187Z","iopub.status.busy":"2024-05-30T19:21:25.857411Z","iopub.status.idle":"2024-05-30T19:21:28.427913Z","shell.execute_reply":"2024-05-30T19:21:28.427111Z","shell.execute_reply.started":"2024-05-30T19:21:25.858149Z"},"id":"6jMDHF0XJU6Y"},"outputs":[],"source":["df_refugee_train.text = df_refugee_train['text'].apply(data_processing)\n","df_refugee_train = df_refugee_train.drop_duplicates('text')"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:28.429195Z","iopub.status.busy":"2024-05-30T19:21:28.428927Z","iopub.status.idle":"2024-05-30T19:21:29.204894Z","shell.execute_reply":"2024-05-30T19:21:29.203917Z","shell.execute_reply.started":"2024-05-30T19:21:28.429172Z"},"id":"s9texKRTJU8E"},"outputs":[],"source":["df_isr_pal_train.text = df_isr_pal_train['text'].apply(data_processing)\n","df_isr_pal_train = df_isr_pal_train.drop_duplicates('text')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:21:29.207884Z","iopub.status.busy":"2024-05-30T19:21:29.207500Z","iopub.status.idle":"2024-05-30T19:21:29.214720Z","shell.execute_reply":"2024-05-30T19:21:29.213763Z","shell.execute_reply.started":"2024-05-30T19:21:29.207852Z"},"executionInfo":{"elapsed":37,"status":"ok","timestamp":1724350439239,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"lznrbIU2JU-b","outputId":"c99c7e4c-4881-4407-9978-a63608c1e26c"},"outputs":[{"output_type":"stream","name":"stdout","text":["yunan ülkemizi işgal etmiş namusumuza tecavüz ediyor onların ağzına sictik denize döktük demiyorsunuz osmanlıyı kotuluyorsunuzdörtyüz sene gücünüze gittihala acıyormu \n","\n","feramuzkartal neslihan düşüncen dogru konuya yanlış yerden bakıyorsunrusya ukranya nato üyesi değil yunanistan türkiye savaşı olursa iki ülke nato üyesinasıl olacak ? atina kara sınırını mile çıkartır işte zaman savaş başlar günde teslim alırbuna ülke bile destek vermez \n","\n","ufukuras adaların silahlandırılması savaş sebebidir tabi öncelik diplomasidir fakat yunanistan var olan antlaşmayı yok sayıp adaları silahlandırıyordiplomasiden sonuç alınmazsa savaşırsınız durumu kabullenirsiniz \n","\n","muratasm izmire gavur diyen keske yunan kazansaydı diyen ermeni tohumlarına birakacaz zannediyorsunuz güzel izmiri bütün hainleri denize yine dökeriz sıkıntı yok yaşa mustafa kemal paşa yaşa \n","\n","bugünden yıl önce izmir ’ yunan işgal birliklerine ilk kurşunu atan milli mücadele ’ ateşinı yakan kahraman hasantahsin ‘ i saygı rahmet minnetle anıyorum \n","\n"]}],"source":["print(df_tr_gr_train[\"text\"].iloc[0], \"\\n\")\n","print(df_tr_gr_train[\"text\"].iloc[1], \"\\n\")\n","print(df_tr_gr_train[\"text\"].iloc[2], \"\\n\")\n","print(df_tr_gr_train[\"text\"].iloc[3], \"\\n\")\n","print(df_tr_gr_train[\"text\"].iloc[4], \"\\n\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"execution":{"iopub.execute_input":"2024-05-30T19:21:29.216267Z","iopub.status.busy":"2024-05-30T19:21:29.215922Z","iopub.status.idle":"2024-05-30T19:21:29.232587Z","shell.execute_reply":"2024-05-30T19:21:29.231600Z","shell.execute_reply.started":"2024-05-30T19:21:29.216242Z"},"executionInfo":{"elapsed":36,"status":"ok","timestamp":1724350439239,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"NNzZYa0hJgR7","outputId":"8511dc6a-ec42-4e18-c4a3-8734760dc571"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       id                                               text  \\\n","0     1568524118357864451  yunan ülkemizi işgal etmiş namusumuza tecavüz ...   \n","1     1527761356061134851  feramuzkartal neslihan düşüncen dogru konuya y...   \n","2     1503008593372749826  ufukuras adaların silahlandırılması savaş sebe...   \n","3     1568255642095296513  muratasm izmire gavur diyen keske yunan kazans...   \n","4     1525787544083910656  bugünden yıl önce izmir ’ yunan işgal birlikle...   \n","...                   ...                                                ...   \n","8452  1393434648647192577  kudüseyürüyoruz sponsoru şeytan olan katil siy...   \n","8453  1515487032570789894  mübarek günlerin hürmetine özgürleştir ilk kıb...   \n","8454  1391751856884523008  bugün ibadethaneye saldırılıyor yalnızca ibade...   \n","8455  1514963851342663684  kurşun atıyorsunuz işlemiyor ! çocuklarımızı k...   \n","8456  1392403022903488516  uyan ey müslüman ! ilk kıblen mübarek mescidi ...   \n","\n","      label  \n","0         1  \n","1         0  \n","2         0  \n","3         1  \n","4         0  \n","...     ...  \n","8452      1  \n","8453      0  \n","8454      0  \n","8455      1  \n","8456      0  \n","\n","[8457 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-4516c58c-0dd2-4876-a412-35668ed29d06\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1568524118357864451</td>\n","      <td>yunan ülkemizi işgal etmiş namusumuza tecavüz ...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1527761356061134851</td>\n","      <td>feramuzkartal neslihan düşüncen dogru konuya y...</td>\n","      <td>0</td>\n","    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value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data.drop('id', inplace=True, axis=1)\n","<ipython-input-21-28d35b452c16>:4: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[\"label\"] = data[\"label\"].apply(lambda x: \"negatif\" if x==1 else \"pozitif\")\n","<ipython-input-21-28d35b452c16>:5: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data.rename(columns={\"text\": \"input\"}, inplace=True)\n"]},{"output_type":"execute_result","data":{"text/plain":["                                         instruction  \\\n","0     Metni negatif veya pozitif olarak sınıflandır.   \n","1     Metni negatif veya pozitif olarak sınıflandır.   \n","2     Metni negatif veya pozitif olarak sınıflandır.   \n","3     Metni negatif veya pozitif olarak sınıflandır.   \n","4     Metni negatif veya pozitif olarak sınıflandır.   \n","...                                              ...   \n","8452  Metni negatif veya pozitif olarak sınıflandır.   \n","8453  Metni negatif veya pozitif olarak sınıflandır.   \n","8454  Metni negatif veya pozitif olarak sınıflandır.   \n","8455  Metni negatif veya pozitif olarak sınıflandır.   \n","8456  Metni negatif veya pozitif olarak sınıflandır.   \n","\n","                                                  input    label  \n","0     yunan ülkemizi işgal etmiş namusumuza tecavüz ...  negatif  \n","1     feramuzkartal neslihan düşüncen dogru konuya y...  pozitif  \n","2     ufukuras adaların silahlandırılması savaş sebe...  pozitif  \n","3     muratasm izmire gavur diyen keske yunan kazans...  negatif  \n","4     bugünden yıl önce izmir ’ yunan işgal birlikle...  pozitif  \n","...                                                 ...      ...  \n","8452  kudüseyürüyoruz sponsoru şeytan olan katil siy...  negatif  \n","8453  mübarek günlerin hürmetine özgürleştir ilk kıb...  pozitif  \n","8454  bugün ibadethaneye saldırılıyor yalnızca ibade...  pozitif  \n","8455  kurşun atıyorsunuz işlemiyor ! çocuklarımızı k...  negatif  \n","8456  uyan ey müslüman ! ilk kıblen mübarek mescidi ...  pozitif  \n","\n","[8430 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-5c1b3a45-2efa-4795-8d99-2faca90d1def\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th 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<th>8454</th>\n","      <td>Metni negatif veya pozitif olarak sınıflandır.</td>\n","      <td>bugün ibadethaneye saldırılıyor yalnızca ibade...</td>\n","      <td>pozitif</td>\n","    </tr>\n","    <tr>\n","      <th>8455</th>\n","      <td>Metni negatif veya pozitif olarak sınıflandır.</td>\n","      <td>kurşun atıyorsunuz işlemiyor ! çocuklarımızı k...</td>\n","      <td>negatif</td>\n","    </tr>\n","    <tr>\n","      <th>8456</th>\n","      <td>Metni negatif veya pozitif olarak sınıflandır.</td>\n","      <td>uyan ey müslüman ! ilk kıblen mübarek mescidi ...</td>\n","      <td>pozitif</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8430 rows × 3 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c1b3a45-2efa-4795-8d99-2faca90d1def')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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Configuration"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:29.303903Z","iopub.status.busy":"2024-05-30T19:21:29.303576Z","iopub.status.idle":"2024-05-30T19:21:29.310096Z","shell.execute_reply":"2024-05-30T19:21:29.309146Z","shell.execute_reply.started":"2024-05-30T19:21:29.303874Z"},"id":"T2VzG8v1Jql1"},"outputs":[],"source":["def create_bnb_config(load_in_4bit, bnb_4bit_use_double_quant, bnb_4bit_quant_type, bnb_4bit_compute_dtype):\n","    \"\"\"\n","    Configures model quantization method using bitsandbytes to speed up training and inference\n","\n","    :param load_in_4bit: Load model in 4-bit precision mode\n","    :param bnb_4bit_use_double_quant: Nested quantization for 4-bit model\n","    :param bnb_4bit_quant_type: Quantization data type for 4-bit model\n","    :param bnb_4bit_compute_dtype: Computation data type for 4-bit model\n","    \"\"\"\n","\n","    bnb_config = BitsAndBytesConfig(\n","        load_in_4bit = load_in_4bit,\n","        bnb_4bit_use_double_quant = bnb_4bit_use_double_quant,\n","        bnb_4bit_quant_type = bnb_4bit_quant_type,\n","        bnb_4bit_compute_dtype = bnb_4bit_compute_dtype,\n","    )\n","\n","    return bnb_config"]},{"cell_type":"markdown","metadata":{"id":"mkZBwDZaJywl"},"source":["## Loading Hugging Face Model and Tokenizer"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:29.311684Z","iopub.status.busy":"2024-05-30T19:21:29.311307Z","iopub.status.idle":"2024-05-30T19:21:29.318786Z","shell.execute_reply":"2024-05-30T19:21:29.317872Z","shell.execute_reply.started":"2024-05-30T19:21:29.311654Z"},"id":"vwi3CqbAJj1m"},"outputs":[],"source":["def load_model(model_name, bnb_config):\n","    \"\"\"\n","    Loads model and model tokenizer\n","\n","    :param model_name: Hugging Face model name\n","    :param bnb_config: Bitsandbytes configuration\n","    \"\"\"\n","\n","    # Get number of GPU device and set maximum memory\n","    n_gpus = torch.cuda.device_count()\n","    max_memory = f'{40960}MB'\n","\n","    # Load model\n","    model = AutoModelForCausalLM.from_pretrained(\n","        model_name,\n","        quantization_config = bnb_config,\n","        device_map = \"auto\", # dispatch the model efficiently on the available resources\n","        torch_dtype=torch.bfloat16,\n","        max_memory = {i: max_memory for i in range(n_gpus)},\n","    )\n","\n","    # Load model tokenizer with the user authentication token\n","    tokenizer = AutoTokenizer.from_pretrained(model_name)\n","\n","    # Set padding token as EOS token\n","    tokenizer.pad_token = tokenizer.eos_token\n","    tokenizer.padding_side = \"right\"\n","\n","    return model, tokenizer"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:21:29.320219Z","iopub.status.busy":"2024-05-30T19:21:29.319942Z","iopub.status.idle":"2024-05-30T19:21:29.329433Z","shell.execute_reply":"2024-05-30T19:21:29.328417Z","shell.execute_reply.started":"2024-05-30T19:21:29.320197Z"},"id":"8mKR3Z3xJgWk"},"outputs":[],"source":["################################################################################\n","# transformers parameters\n","################################################################################\n","\n","# The pre-trained model from the Hugging Face Hub to load and fine-tune\n","model_name = \"Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0\"\n","\n","################################################################################\n","# bitsandbytes parameters\n","################################################################################\n","\n","# Activate 4-bit precision base model loading\n","load_in_4bit = True\n","\n","# Activate nested quantization for 4-bit base models (double quantization)\n","bnb_4bit_use_double_quant = True\n","\n","# Quantization type (fp4 or nf4)\n","bnb_4bit_quant_type = \"nf4\"\n","\n","# Compute data type for 4-bit base models\n","bnb_4bit_compute_dtype = torch.bfloat16"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":528,"referenced_widgets":["a1d9d64fc9ce41b58ca98f6435a76878","7509df475a0540d1933ce6856200d57c","623e8612fd774c39baa475684092af23","924d562e77d64597aea48c82c1038155","818e0fbbb454427d99cc150da5a835bb","8d0177bb30aa470289aa70cb16c4c195","831d899bf33644bf90139631bd6c1eac","bcec1e3a4e0e49caaeab603cbd4ae891","9b53a745fa46458da925c160b8e2c5d7","4621512048ed45fb8695679ea9188b51","faad3f9ac11e4f6d97c39d757365d5b0","e8c5546f6d09444e8898af31826ffe3e","03b78cc34e8442c1bbb67a4783008b88","7048dcfbe36145d8b7719998e27f0c0a","8dc7f21c3dbc4a3f975708a58e717385","662bdf818d0c43f8b67ac173fd7b203e","4055719e5f214a8e92524b7303c450f3","86e859aff95f49f2b853c8b327789499","290507415e6c476d890e0b474a847910","c784621823f646de941453f645903bfd","57c074fcea47410ebdabefd4cb57f57e","b99a379e124e4e8899b3ef067a816479","4b50c710842944b28ba1ebab42fd4115","4558446c24f14062a05e81c8b5efd14a","062219d76268418799b673b1d4769d1b","9eb6f76323ca42b5b7a66a1e855b4aec","ad951b56d83b4c01b8715252e1e8be9f","ec474a9bda8648e7b4d0c3000cb5b69d","566d802614724929add7f049c5cf02ed","29e8ec73c7614050bb175cfadc373d98","73cbc6f521e047a496703e4057a4f1df","5c29ab8c60e642c7b23c839b3192b669","a6dd567968df4132a7cddf776bef5847","22272915b6e54ef59b64314b3a4780df","4f91c2cae76b461fbdee54af142827c7","84252f39b0c6426db5d335cfe174190e","bd2930ad0ae54515affe76e1e9837aae","e95efb9a10dd4bcc89424349cdc20a84","de3a922e1fa84cc29e49836d02c11d27","7ffb8e90967044e5acc61b0fcf0f6d85","d403e3c91a7a40d6a5dc0294b085fbbd","ba174a3ce76345f5a584144298060e2b","4c2d8469b28b480888702dc479f39709","353360f946c64d9b877a0cd664a21dc3","6d881079326d43138eeb8ac896986cc0","84c945626bd64b3593ca0205ea65f3cb","03261ea3da064ef690f77785fcecbde8","0ee9c8f0b88a48dc8a25d6e4489db8fa","cf9583d45cc54613bac6c1109e9a61b4","33f88f546d204f078884451f94f4e368","3836b50df00f4983aa90bee4c81cb08b","3a5d281afab54d3daff381c4e97b5ca5","df27962d73344fc09b757689af1a5355","4d4deca105de4e2f9d00a28cc662eeec","d313631107e447d9aee3096064b145f3","4b61c58ddebe409e8c439c466df46c04","5869d8c3d76e4756a1344703bad84829","c99bf70b4ad9492e90628fce4ab6130f","4b43231ee4c243c69ed56ddeb7c1ec68","a83eae1da48b49fa86aa64a604d01436","ddb6d992f137471f9d30f164637557d5","c8836d3be9d24fc5b5f2cb949fb14378","b3d21b2fd1c94f86aced15631cfa35dc","d199c1fbf2744fa1a694b8e4e6e9421b","80243bd9d9394667bf0d887ce5c2736d","4536d1e855bf43b9a9a08e7d4f0eb3f4","9b4f40aa9a9e474a93929cce0506181f","e59c7aaeea1b4a678b191ea7c95bc605","5968bbcba202498894e964b349ffb937","5cf1ffe0b89f49ab908c270dc15ccc69","f93104453fdd44a7a95d879033df0f4f","af6d118f9a70411b9b5877b6b2eb088f","713a3ecf0ffe49028982086932f83928","2cdf441a3f344be3bb7a7daef10526f1","47036ded3e664106a24515ec11ccaba7","5af588f28c6d428091720d161d27cca8","e4e4e973be944fea860042f0cb893dc1","440371a48bc0471689c7124d14015c7a","2ecb63cdaad54096b1fd94fbbf1cc0af","8ea5f777d4eb4069bee4235916994aa9","4e9f827668f5402294047e5821ca462c","208ed0dbc1414d1680db85d0434b2650","d9514e34704e45d9aacee74acd680496","c5efae178be94cd98cd750c532461c9d","c8b0cd384864447ea9ba1caa34278515","8fb15889cc554a50a6fe86ffdb9e824c","bd9faec8fdb34bffb206b0e8fd9c4cff","b71eb607c73c476fac2f951befe8d7a6","5dd30ae657aa4bdba400c0dd49b4babc","12b04cb4ff094e5982b5c8361aa53434","957fe0c9857444aead5aa6909e6e2912","46642b788fa84532bcf22041749cddf8","45642c465c624c558781d6cf68616c2a","19283f35101e4c52b4072d93cf73ea2e","2de67774e2ba4bc8972a96e1112db8d5","c7fd49cde8894d12ae6e1a50a9fb44fa","7ec7dcdf18074deea45750d591743dd0","bed1b659d61d45f0aa95aa174b355de8","e64a7d4599e8412ebff0760baa50f515","7e7b5591d93344e69323a1434168c1c5","b34b526b5d524ae5affedfd375c8b524","8b7c7b26f13d428bb3f2307ddcdc5236","1ebc66dc0f3c4cd4a48101ec47dd9f83","1810a19444ae466f97854b0054148349","a0c35e3035ef4e179c81790722312eda","4836ecf5f62c4898af20f8e1d8220533","4b9eea26f50a42b2b84e6ada660d7556","62d7260b96994127981874ccc7925934","47e096c2adc04e0cbcb70a1c0ae2efd7","dbea717a85ac4d4c8b6fe8e47e1a6cd1","55d7e3a3a5ac45d8b3ca000be5bd96e7","2bbbad4021e74d20aed926c236ab9e8a","66e0f63e11384a5a954d16632aeb1d5d","05d7aca812e64810bc6409b2c7193a33","cfbf35fd6f794231a2431ac14546e13e","dd9d391ce79146c18ca6eea946909f5d","21e7c325ef864acabd0a73ea06adb291","9d0595e564fa436a8a98d2a057f5d854","13573efdb88f4b6193408d45b9abfdbd","79ff9eb5ffe34d719043b7bd57cfc57b","2f4e50534bb241e8a289e6530231d367"]},"execution":{"iopub.execute_input":"2024-05-30T19:21:29.331480Z","iopub.status.busy":"2024-05-30T19:21:29.331033Z","iopub.status.idle":"2024-05-30T19:23:12.509603Z","shell.execute_reply":"2024-05-30T19:23:12.508793Z","shell.execute_reply.started":"2024-05-30T19:21:29.331447Z"},"executionInfo":{"elapsed":169491,"status":"ok","timestamp":1724350609186,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"XvXZfoC4JgZD","outputId":"f5e0289a-2b33-4e77-fa58-ab0601b16e65"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/655 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1d9d64fc9ce41b58ca98f6435a76878"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e8c5546f6d09444e8898af31826ffe3e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4b50c710842944b28ba1ebab42fd4115"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00003.safetensors:   0%|          | 0.00/4.93G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"22272915b6e54ef59b64314b3a4780df"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00003.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6d881079326d43138eeb8ac896986cc0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00003.safetensors:   0%|          | 0.00/4.84G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4b61c58ddebe409e8c439c466df46c04"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9b4f40aa9a9e474a93929cce0506181f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"440371a48bc0471689c7124d14015c7a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5dd30ae657aa4bdba400c0dd49b4babc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/712k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7e7b5591d93344e69323a1434168c1c5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/551 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"55d7e3a3a5ac45d8b3ca000be5bd96e7"}},"metadata":{}}],"source":["# Load model from Hugging Face Hub with model name and bitsandbytes configuration\n","\n","bnb_config = create_bnb_config(load_in_4bit, bnb_4bit_use_double_quant, bnb_4bit_quant_type, bnb_4bit_compute_dtype)\n","\n","model, tokenizer = load_model(model_name, bnb_config)\n","\n","model, tokenizer = setup_chat_format(model, tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:12.511958Z","iopub.status.busy":"2024-05-30T19:23:12.511672Z","iopub.status.idle":"2024-05-30T19:23:12.520463Z","shell.execute_reply":"2024-05-30T19:23:12.519561Z","shell.execute_reply.started":"2024-05-30T19:23:12.511934Z"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1724350609186,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"Z3aoa4CHJgbk","outputId":"60cd6633-25df-4d78-fe04-b874e64aa353"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["MistralForCausalLM(\n","  (model): MistralModel(\n","    (embed_tokens): Embedding(44314, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x MistralDecoderLayer(\n","        (self_attn): MistralAttention(\n","          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n","          (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n","          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (rotary_emb): MistralRotaryEmbedding()\n","        )\n","        (mlp): MistralMLP(\n","          (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n","          (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n","          (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n","          (act_fn): SiLU()\n","        )\n","        (input_layernorm): MistralRMSNorm()\n","        (post_attention_layernorm): MistralRMSNorm()\n","      )\n","    )\n","    (norm): MistralRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=44314, bias=False)\n",")"]},"metadata":{},"execution_count":28}],"source":["model"]},{"cell_type":"markdown","metadata":{"id":"eNX_vBV1PhB_"},"source":["### Dataset\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:16.554233Z","iopub.status.busy":"2024-05-30T19:23:16.553346Z","iopub.status.idle":"2024-05-30T19:23:16.654437Z","shell.execute_reply":"2024-05-30T19:23:16.653426Z","shell.execute_reply.started":"2024-05-30T19:23:16.554200Z"},"id":"1NuqRQf-PecG"},"outputs":[],"source":["df_train.to_csv('hate_classification.csv', index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:16.720731Z","iopub.status.busy":"2024-05-30T19:23:16.720406Z","iopub.status.idle":"2024-05-30T19:23:16.725747Z","shell.execute_reply":"2024-05-30T19:23:16.724806Z","shell.execute_reply.started":"2024-05-30T19:23:16.720705Z"},"id":"7CJgLN07Pee_"},"outputs":[],"source":["dataset_name = \"/content/hate_classification.csv\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118,"referenced_widgets":["0072479a4fbc4159b050663e8d21cbbc","1819e499a6184b5eaf16c467dbcb47e1","e05b842d9eb44cb68ae7d613d7283d5d","1dc99a88eaac499197f71380e8c0032f","7c126792a3254846b8625f16337fd92b","6ec34d7a5a5a45cca3df46c3451e6664","34a83724d143453a9f0cfffdffdcc64d","48d547858d3d41988fc0d88dfcfb1a5d","0e0870fbd5a44563abf73f38ad342306","d55c194ce68c4ed0a70d670a0a5afe3b","3aff75233f6a40419209223d4a46855e"]},"execution":{"iopub.execute_input":"2024-05-30T19:23:16.916125Z","iopub.status.busy":"2024-05-30T19:23:16.915626Z","iopub.status.idle":"2024-05-30T19:23:17.172792Z","shell.execute_reply":"2024-05-30T19:23:17.171923Z","shell.execute_reply.started":"2024-05-30T19:23:16.916101Z"},"executionInfo":{"elapsed":1090,"status":"ok","timestamp":1724350616228,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"bk9JWlt4Pejz","outputId":"30a6c626-495b-43ac-b849-f5ea7b7df4fa"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0072479a4fbc4159b050663e8d21cbbc"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['instruction', 'input', 'label'],\n","    num_rows: 1000\n","})"]},"metadata":{},"execution_count":31}],"source":["dataset = load_dataset(\"csv\", data_files = dataset_name, split = \"train\").shuffle().select(range(1000))\n","dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:17.174477Z","iopub.status.busy":"2024-05-30T19:23:17.174190Z","iopub.status.idle":"2024-05-30T19:23:17.180211Z","shell.execute_reply":"2024-05-30T19:23:17.179302Z","shell.execute_reply.started":"2024-05-30T19:23:17.174451Z"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1724350616228,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"R_lxEuX6PlkX","outputId":"3cc99239-2c4a-4ffa-b6d1-e64b84a77fd8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['instruction', 'input', 'label'],\n","    num_rows: 1000\n","})"]},"metadata":{},"execution_count":32}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:17.249418Z","iopub.status.busy":"2024-05-30T19:23:17.249139Z","iopub.status.idle":"2024-05-30T19:23:17.267118Z","shell.execute_reply":"2024-05-30T19:23:17.266353Z","shell.execute_reply.started":"2024-05-30T19:23:17.249394Z"},"id":"bOIIET5nPlnI"},"outputs":[],"source":["dataset = dataset.train_test_split(test_size=500/1000)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:17.463213Z","iopub.status.busy":"2024-05-30T19:23:17.462938Z","iopub.status.idle":"2024-05-30T19:23:17.468919Z","shell.execute_reply":"2024-05-30T19:23:17.467961Z","shell.execute_reply.started":"2024-05-30T19:23:17.463189Z"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1724350616229,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"CMDXvjPiPlqL","outputId":"c3e8bd13-85e3-4b1b-ea1f-c0359b35d858"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['instruction', 'input', 'label'],\n","        num_rows: 500\n","    })\n","    test: Dataset({\n","        features: ['instruction', 'input', 'label'],\n","        num_rows: 500\n","    })\n","})"]},"metadata":{},"execution_count":34}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:17.784894Z","iopub.status.busy":"2024-05-30T19:23:17.784603Z","iopub.status.idle":"2024-05-30T19:23:17.789346Z","shell.execute_reply":"2024-05-30T19:23:17.788400Z","shell.execute_reply.started":"2024-05-30T19:23:17.784872Z"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1724350617578,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"gw98phNbPls-","outputId":"0ca1750e-8c4a-40f3-ffff-3a315cf89cdf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of prompts: 2\n","Column names are: {'train': ['instruction', 'input', 'label'], 'test': ['instruction', 'input', 'label']}\n"]}],"source":["print(f'Number of prompts: {len(dataset)}')\n","print(f'Column names are: {dataset.column_names}')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:18.028850Z","iopub.status.busy":"2024-05-30T19:23:18.028528Z","iopub.status.idle":"2024-05-30T19:23:18.038963Z","shell.execute_reply":"2024-05-30T19:23:18.038034Z","shell.execute_reply.started":"2024-05-30T19:23:18.028826Z"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1724350617579,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"qmnW5vL3Prog","outputId":"167e3066-0b6c-451f-ca36-0abeacf19402"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'instruction': 'Metni negatif veya pozitif olarak sınıflandır.',\n"," 'input': 'kendi ülkemde endişe içinde yaşiyorum ülkemde afgan suriyeli istemiyorum can güvenliğimiz yok baksan yanbaktın öldürüyolar artık yeter birileri durdesin ülkemizimi terkedelim',\n"," 'label': 'pozitif'}"]},"metadata":{},"execution_count":36}],"source":["dataset[\"train\"][randrange(len(dataset))]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:18.230650Z","iopub.status.busy":"2024-05-30T19:23:18.230387Z","iopub.status.idle":"2024-05-30T19:23:18.236304Z","shell.execute_reply":"2024-05-30T19:23:18.235454Z","shell.execute_reply.started":"2024-05-30T19:23:18.230629Z"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1724350617579,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"1enHoi7HPrrl","outputId":"3f71644a-ffa6-45d3-bdf2-be9146fed7bb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'instruction': 'Metni negatif veya pozitif olarak sınıflandır.',\n"," 'input': 'sadece yıkılan binalar uğraşmıyoruz sadece vefat edenlere üzülmüyoruz yıkılan ahlak yıkılan vicdan hala yaşayan insan artıklarına üzülüyoruz utanıyorum sizin adınıza yağmacılar abiyegönderenler',\n"," 'label': 'negatif'}"]},"metadata":{},"execution_count":37}],"source":["dataset[\"train\"][5]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:18.522206Z","iopub.status.busy":"2024-05-30T19:23:18.521930Z","iopub.status.idle":"2024-05-30T19:23:18.527496Z","shell.execute_reply":"2024-05-30T19:23:18.526699Z","shell.execute_reply.started":"2024-05-30T19:23:18.522182Z"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1724350617579,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"ym8pXdG5PruX","outputId":"ab3640fd-b021-4799-ef45-24a587793454"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['instruction', 'input', 'label'],\n","    num_rows: 500\n","})"]},"metadata":{},"execution_count":38}],"source":["train = dataset[\"train\"]\n","train"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:18.699888Z","iopub.status.busy":"2024-05-30T19:23:18.699606Z","iopub.status.idle":"2024-05-30T19:23:18.705847Z","shell.execute_reply":"2024-05-30T19:23:18.704899Z","shell.execute_reply.started":"2024-05-30T19:23:18.699865Z"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1724350617579,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"jIuot6B2Plv8","outputId":"03f9814c-ff3a-40e0-d1ac-fe0e1c404265"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['instruction', 'input', 'label'],\n","    num_rows: 500\n","})"]},"metadata":{},"execution_count":39}],"source":["test = dataset[\"test\"]\n","test"]},{"cell_type":"markdown","metadata":{"id":"pYOQxesVP09I"},"source":["### Creating Prompt Template"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:19.630009Z","iopub.status.busy":"2024-05-30T19:23:19.629407Z","iopub.status.idle":"2024-05-30T19:23:19.636805Z","shell.execute_reply":"2024-05-30T19:23:19.635775Z","shell.execute_reply.started":"2024-05-30T19:23:19.629979Z"},"id":"37sc_mb5Pxx5"},"outputs":[],"source":["def create_prompt_formats(sample):\n","    \"\"\"\n","    Creates a formatted prompt template for a prompt in the instruction dataset\n","\n","    :param sample: Prompt or sample from the instruction dataset\n","    \"\"\"\n","\n","    # Initialize static strings for the prompt template\n","    INTRO_BLURB = \"[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\"\n","    INSTRUCTION_KEY = \"Talimat:\"\n","    INPUT_KEY = \"Metin:\"\n","    RESPONSE_KEY = \"Yanıt:\"\n","    END_KEY = \"[/INST]\"\n","\n","    # Combine a prompt with the static strings\n","    blurb = f\"{INTRO_BLURB}\"\n","    instruction = f\"{INSTRUCTION_KEY}\\n{sample['instruction']}\"\n","    input_context = f\"{INPUT_KEY}\\n{sample['input']}\" if sample[\"input\"] else None\n","    response = f\"{RESPONSE_KEY}{sample['label']}\"\n","    end = f\"{END_KEY}\"\n","\n","    # Create a list of prompt template elements\n","    parts = [part for part in [blurb, instruction, input_context, response, end] if part]\n","\n","    # Join prompt template elements into a single string to create the prompt template\n","    formatted_prompt = \"\\n\\n\".join(parts)\n","\n","    # Store the formatted prompt template in a new key \"text\"\n","    sample[\"text\"] = formatted_prompt\n","\n","    return sample"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:19.876042Z","iopub.status.busy":"2024-05-30T19:23:19.875357Z","iopub.status.idle":"2024-05-30T19:23:19.881821Z","shell.execute_reply":"2024-05-30T19:23:19.881045Z","shell.execute_reply.started":"2024-05-30T19:23:19.876017Z"},"id":"o6JRyEjRI9ye"},"outputs":[],"source":["def create_prompt_test_formats(sample):\n","    \"\"\"\n","    Creates a formatted prompt template for a prompt in the instruction dataset\n","\n","    :param sample: Prompt or sample from the instruction dataset\n","    \"\"\"\n","\n","    # Initialize static strings for the prompt template\n","    INTRO_BLURB = \"[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\"\n","    INSTRUCTION_KEY = \"Talimat:\"\n","    INPUT_KEY = \"Metin:\"\n","    RESPONSE_KEY = \"Yanıt:\"\n","\n","    # Combine a prompt with the static strings\n","    blurb = f\"{INTRO_BLURB}\"\n","    instruction = f\"{INSTRUCTION_KEY}\\n{sample['instruction']}\"\n","    input_context = f\"{INPUT_KEY}\\n{sample['input']}\" if sample[\"input\"] else None\n","    response = f\"{RESPONSE_KEY}\"\n","\n","    # Create a list of prompt template elements\n","    parts = [part for part in [blurb, instruction, input_context, response] if part]\n","\n","    # Join prompt template elements into a single string to create the prompt template\n","    formatted_prompt = \"\\n\\n\".join(parts)\n","\n","    # Store the formatted prompt template in a new key \"text\"\n","    sample[\"text\"] = formatted_prompt\n","\n","    return sample"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:20.064572Z","iopub.status.busy":"2024-05-30T19:23:20.064087Z","iopub.status.idle":"2024-05-30T19:23:20.070389Z","shell.execute_reply":"2024-05-30T19:23:20.069434Z","shell.execute_reply.started":"2024-05-30T19:23:20.064535Z"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1724350618454,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"NHCEnL_rPx0d","outputId":"40be0a46-d3e8-4950-9e2a-96189133ba76"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'instruction': 'Metni negatif veya pozitif olarak sınıflandır.',\n"," 'input': 'kendi ülkemde endişe içinde yaşiyorum ülkemde afgan suriyeli istemiyorum can güvenliğimiz yok baksan yanbaktın öldürüyolar artık yeter birileri durdesin ülkemizimi terkedelim',\n"," 'label': 'pozitif',\n"," 'text': '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nMetin:\\nkendi ülkemde endişe içinde yaşiyorum ülkemde afgan suriyeli istemiyorum can güvenliğimiz yok baksan yanbaktın öldürüyolar artık yeter birileri durdesin ülkemizimi terkedelim\\n\\nYanıt:pozitif\\n\\n[/INST]'}"]},"metadata":{},"execution_count":42}],"source":["create_prompt_formats(train[randrange(len(dataset))])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:20.442396Z","iopub.status.busy":"2024-05-30T19:23:20.442038Z","iopub.status.idle":"2024-05-30T19:23:20.449179Z","shell.execute_reply":"2024-05-30T19:23:20.448250Z","shell.execute_reply.started":"2024-05-30T19:23:20.442356Z"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1724350618454,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"MzNM-BdAPx3C","outputId":"82b8625b-eb8d-4644-b680-601fcd043d7e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'instruction': 'Metni negatif veya pozitif olarak sınıflandır.',\n"," 'input': 'user milyonluk ülkede milyon test herşey buna göre ölçülmüş suriyeliler kayıp yabamcılar kayıp sayı hangi şehir kaç kişi kayıp evine git kendi karantinananı al söylemenler kayıp şekilde gömüldü kayıp iyi oran',\n"," 'label': 'pozitif',\n"," 'text': '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nMetin:\\nuser milyonluk ülkede milyon test herşey buna göre ölçülmüş suriyeliler kayıp yabamcılar kayıp sayı hangi şehir kaç kişi kayıp evine git kendi karantinananı al söylemenler kayıp şekilde gömüldü kayıp iyi oran\\n\\nYanıt:'}"]},"metadata":{},"execution_count":43}],"source":["create_prompt_test_formats(test[randrange(len(dataset))])"]},{"cell_type":"markdown","metadata":{"id":"xfCbr2uQP91t"},"source":["### Getting Maximum Sequence Length of the Pre-trained Model"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:21.110010Z","iopub.status.busy":"2024-05-30T19:23:21.109705Z","iopub.status.idle":"2024-05-30T19:23:21.116156Z","shell.execute_reply":"2024-05-30T19:23:21.115105Z","shell.execute_reply.started":"2024-05-30T19:23:21.109985Z"},"id":"GDBVdHW4JVBE"},"outputs":[],"source":["def get_max_length(model):\n","    \"\"\"\n","    Extracts maximum token length from the model configuration\n","\n","    :param model: Hugging Face model\n","    \"\"\"\n","\n","    # Pull model configuration\n","    conf = model.config\n","    # Initialize a \"max_length\" variable to store maximum sequence length as null\n","    max_length = None\n","    # Find maximum sequence length in the model configuration and save it in \"max_length\" if found\n","    for length_setting in [\"n_positions\", \"max_position_embeddings\", \"seq_length\"]:\n","        max_length = getattr(model.config, length_setting, None)\n","        if max_length:\n","            print(f\"Found max lenth: {max_length}\")\n","            break\n","    # Set \"max_length\" to 1024 (default value) if maximum sequence length is not found in the model configuration\n","    if not max_length:\n","        max_length = 1024\n","        print(f\"Using default max length: {max_length}\")\n","    return max_length"]},{"cell_type":"markdown","metadata":{"id":"Yef1IHJRTGf1"},"source":["### Preprocessing Dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:21.998247Z","iopub.status.busy":"2024-05-30T19:23:21.997436Z","iopub.status.idle":"2024-05-30T19:23:22.003483Z","shell.execute_reply":"2024-05-30T19:23:22.002570Z","shell.execute_reply.started":"2024-05-30T19:23:21.998214Z"},"id":"tMcCsZa2TB7n"},"outputs":[],"source":["def preprocess_dataset(seed, dataset: str):\n","    \"\"\"\n","    Tokenizes dataset for fine-tuning\n","\n","    :param tokenizer (AutoTokenizer): Model tokenizer\n","    :param max_length (int): Maximum number of tokens to emit from the tokenizer\n","    :param seed: Random seed for reproducibility\n","    :param dataset (str): Instruction dataset\n","    \"\"\"\n","\n","    # Add prompt to each sample\n","    print(\"Preprocessing dataset...\")\n","    dataset = dataset.map(create_prompt_formats)\n","\n","    # Apply preprocessing to each batch of the dataset & and remove \"instruction\", \"input\", \"output\", and \"text\" fields\n","\n","    dataset = dataset.remove_columns([\"instruction\", \"input\", \"label\"])\n","\n","    # Shuffle dataset\n","    dataset = dataset.shuffle(seed = seed)\n","\n","    return dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:22.399387Z","iopub.status.busy":"2024-05-30T19:23:22.398655Z","iopub.status.idle":"2024-05-30T19:23:22.404173Z","shell.execute_reply":"2024-05-30T19:23:22.403281Z","shell.execute_reply.started":"2024-05-30T19:23:22.399357Z"},"id":"GZ-UKrIbTB_R"},"outputs":[],"source":["def preprocess_test_dataset(dataset: str):\n","    \"\"\"\n","    Tokenizes dataset for fine-tuning\n","\n","    :param tokenizer (AutoTokenizer): Model tokenizer\n","    :param max_length (int): Maximum number of tokens to emit from the tokenizer\n","    :param seed: Random seed for reproducibility\n","    :param dataset (str): Instruction dataset\n","    \"\"\"\n","\n","    # Add prompt to each sample\n","    print(\"Preprocessing dataset...\")\n","    dataset = dataset.map(create_prompt_test_formats)\n","\n","    dataset = dataset.remove_columns([\"instruction\", \"input\", \"label\"])\n","\n","    return dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":84,"referenced_widgets":["3dc34d91011941e48f7ed8ef27efe976","1781cd729d094084b1a582d47148c1e8","3e17676669f442d19470a40bb431c7fb","1727bdd50a264b59abde2e94885d3ab4","9b2cab8bf97b4f42924bb7a8858bdd2a","b3c202e4f9bb4d06898fa0df679eb8e5","5c116a6a676b416a8b8909945c700c7a","b72e4e0bb7a44c70bc48a9433ccd1f1d","2000b569f97f4bf595af38295a710d42","f70a5a4b198e436b9e0b081dbef082b2","d7e950baef1547508fef9e8a47f01fc9"]},"execution":{"iopub.execute_input":"2024-05-30T19:23:22.764579Z","iopub.status.busy":"2024-05-30T19:23:22.764078Z","iopub.status.idle":"2024-05-30T19:23:22.866885Z","shell.execute_reply":"2024-05-30T19:23:22.866170Z","shell.execute_reply.started":"2024-05-30T19:23:22.764542Z"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1724350621142,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"0njK3JTHP-n3","outputId":"a8df30a6-8a23-45ed-9606-d3f23f660675"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found max lenth: 32768\n","Preprocessing dataset...\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/500 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3dc34d91011941e48f7ed8ef27efe976"}},"metadata":{}}],"source":["# Random seed\n","seed = 33\n","\n","max_length = get_max_length(model)\n","preprocessed_train = preprocess_dataset(seed, train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:23.272279Z","iopub.status.busy":"2024-05-30T19:23:23.272010Z","iopub.status.idle":"2024-05-30T19:23:23.277848Z","shell.execute_reply":"2024-05-30T19:23:23.276997Z","shell.execute_reply.started":"2024-05-30T19:23:23.272255Z"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1724350621142,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"CssgBq4ZIE8H","outputId":"5ef09337-40a9-4512-8aeb-8bb9d56d0ded"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["32768"]},"metadata":{},"execution_count":48}],"source":["max_length"]},{"cell_type":"code","source":["max_length = 128"],"metadata":{"id":"QIh1hWSWMVdw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Note: 32768 is huge, so due to our hardware limitations, we need to adjust to smaller values. For now, just use 128, which is a much smaller value.**"],"metadata":{"id":"w7-dwhsULwft"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:23.889691Z","iopub.status.busy":"2024-05-30T19:23:23.889314Z","iopub.status.idle":"2024-05-30T19:23:23.894543Z","shell.execute_reply":"2024-05-30T19:23:23.893598Z","shell.execute_reply.started":"2024-05-30T19:23:23.889662Z"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1724350623088,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"tthSnSaBP-ql","outputId":"67ceab52-3e1e-4e7a-c6e4-3126dc00a83e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['text'],\n","    num_rows: 500\n","})\n"]}],"source":["print(preprocessed_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:24.451606Z","iopub.status.busy":"2024-05-30T19:23:24.450680Z","iopub.status.idle":"2024-05-30T19:23:24.456253Z","shell.execute_reply":"2024-05-30T19:23:24.455369Z","shell.execute_reply.started":"2024-05-30T19:23:24.451544Z"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1724350623770,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"uc_3ig1FTs3J","outputId":"731fd282-9463-466d-b61b-e13d00b4ab4c"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'text': '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nMetin:\\nbana bak afgan mısınız sikimsiniz taliban bayrağını kıvırır götünüze sokarız burası kabil değil ananızı sikeriz sizin orospu çocuğu burası türk yurdu başkent ankara hadi siktir olup gidin şimdi\\n\\nYanıt:negatif\\n\\n[/INST]'}\n"]}],"source":["print(preprocessed_train[0])"]},{"cell_type":"code","source":["preprocessed_test = preprocess_test_dataset(test)\n","preprocessed_test"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":136,"referenced_widgets":["258afc4083ad490ab938e8b64bde4eea","7488e94d406f4958bd884de0b50e3fb9","bfafb4c9d5334d6c91aaccacdc11dff5","66f0eea618e94dc5a4e18bc6a359e452","8605fa498d5d499582f8420ca94ae766","fb2e833a57a4432f98ca44a60c77c5fb","8c094d3819404c16afe88f60c28d6707","a25ffacbc5634b29ba089f83a97dd2e8","0d60ba6734ad4eaabd59858d6404ac6c","21b7e88f25ca4ca8bfe7aefd39333484","4d810725a5584bb1b1dfdd44c6b38719"]},"id":"67E6z4XEeyAM","executionInfo":{"status":"ok","timestamp":1724350623770,"user_tz":-180,"elapsed":13,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"}},"outputId":"971b6486-3e55-4e74-9adf-e96d9805d014"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Preprocessing dataset...\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/500 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"258afc4083ad490ab938e8b64bde4eea"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['text'],\n","    num_rows: 500\n","})"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["print(preprocessed_test[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JgFSGbtQe5a4","executionInfo":{"status":"ok","timestamp":1724350623770,"user_tz":-180,"elapsed":10,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"}},"outputId":"385d2606-8b5d-4081-acb0-543edea83f0e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'text': '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nMetin:\\nuser milyonluk ülkede milyon test herşey buna göre ölçülmüş suriyeliler kayıp yabamcılar kayıp sayı hangi şehir kaç kişi kayıp evine git kendi karantinananı al söylemenler kayıp şekilde gömüldü kayıp iyi oran\\n\\nYanıt:'}\n"]}]},{"cell_type":"markdown","metadata":{"id":"l37gaiIBTxc6"},"source":["### Creating PEFT Configuration"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:25.576462Z","iopub.status.busy":"2024-05-30T19:23:25.575686Z","iopub.status.idle":"2024-05-30T19:23:25.582276Z","shell.execute_reply":"2024-05-30T19:23:25.581331Z","shell.execute_reply.started":"2024-05-30T19:23:25.576430Z"},"id":"iAdORhF6TuZX"},"outputs":[],"source":["def create_peft_config(r, lora_alpha, target_modules, lora_dropout, bias, task_type):\n","    \"\"\"\n","    Creates Parameter-Efficient Fine-Tuning configuration for the model\n","\n","    :param r: LoRA attention dimension\n","    :param lora_alpha: Alpha parameter for LoRA scaling\n","    :param modules: Names of the modules to apply LoRA to\n","    :param lora_dropout: Dropout Probability for LoRA layers\n","    :param bias: Specifies if the bias parameters should be trained\n","    \"\"\"\n","    config = LoraConfig(\n","        r = r,\n","        lora_alpha = lora_alpha,\n","        target_modules = target_modules,\n","        lora_dropout = lora_dropout,\n","        bias = bias,\n","        task_type = task_type,\n","    )\n","\n","    return config"]},{"cell_type":"markdown","metadata":{"id":"OhvEfcoqT06d"},"source":["### Finding Modules for LoRA Application"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:26.341516Z","iopub.status.busy":"2024-05-30T19:23:26.341179Z","iopub.status.idle":"2024-05-30T19:23:26.347770Z","shell.execute_reply":"2024-05-30T19:23:26.346694Z","shell.execute_reply.started":"2024-05-30T19:23:26.341489Z"},"id":"zZ1HCzeyTuc1"},"outputs":[],"source":["def find_all_linear_names(model):\n","    \"\"\"\n","    Find modules to apply LoRA to.\n","\n","    :param model: PEFT model\n","    \"\"\"\n","\n","    cls = bnb.nn.Linear4bit\n","    lora_module_names = set()\n","    for name, module in model.named_modules():\n","        if isinstance(module, cls):\n","            names = name.split('.')\n","            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n","\n","    if 'lm_head' in lora_module_names:\n","        lora_module_names.remove('lm_head')\n","    print(f\"LoRA module names: {list(lora_module_names)}\")\n","    return list(lora_module_names)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2024-05-30T19:23:26.791224Z","iopub.status.busy":"2024-05-30T19:23:26.790885Z","iopub.status.idle":"2024-05-30T19:23:26.799133Z","shell.execute_reply":"2024-05-30T19:23:26.798172Z","shell.execute_reply.started":"2024-05-30T19:23:26.791195Z"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1724350624693,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"Dn-DZqe8N5QO","outputId":"005b3ba2-02bb-4ba3-ce55-7d0911a07df3"},"outputs":[{"output_type":"stream","name":"stdout","text":["LoRA module names: ['v_proj', 'k_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj', 'q_proj']\n"]},{"output_type":"execute_result","data":{"text/plain":["['v_proj', 'k_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj', 'q_proj']"]},"metadata":{},"execution_count":56}],"source":["find_all_linear_names(model)"]},{"cell_type":"markdown","metadata":{"id":"ZMjkqgHCLQ5o"},"source":["### Calculating Trainable Parameters\n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:27.979321Z","iopub.status.busy":"2024-05-30T19:23:27.978662Z","iopub.status.idle":"2024-05-30T19:23:27.985478Z","shell.execute_reply":"2024-05-30T19:23:27.984485Z","shell.execute_reply.started":"2024-05-30T19:23:27.979290Z"},"id":"ZYbJ8Z2uLR62"},"outputs":[],"source":["def print_trainable_parameters(model, use_4bit = False):\n","    \"\"\"\n","    Prints the number of trainable parameters in the model.\n","\n","    :param model: PEFT model\n","    \"\"\"\n","\n","    trainable_params = 0\n","    all_param = 0\n","\n","    for _, param in model.named_parameters():\n","        num_params = param.numel()\n","        if num_params == 0 and hasattr(param, \"ds_numel\"):\n","            num_params = param.ds_numel\n","        all_param += num_params\n","        if param.requires_grad:\n","            trainable_params += num_params\n","\n","    if use_4bit:\n","        trainable_params /= 2\n","\n","    print(\n","        f\"All Parameters: {all_param:,d} || Trainable Parameters: {trainable_params:,d} || Trainable Parameters %: {100 * trainable_params / all_param}\"\n","    )"]},{"cell_type":"markdown","metadata":{"id":"hYf6WWMxT5vf"},"source":["### Fine-tuning the Pre-trained Model\n"]},{"cell_type":"code","source":["output_dir=\"trained_weigths2\""],"metadata":{"id":"xELWkZ3mW8WV"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:28.568121Z","iopub.status.busy":"2024-05-30T19:23:28.567809Z","iopub.status.idle":"2024-05-30T19:23:28.577891Z","shell.execute_reply":"2024-05-30T19:23:28.576884Z","shell.execute_reply.started":"2024-05-30T19:23:28.568094Z"},"id":"nUcrorLYUjGt"},"outputs":[],"source":["args = TrainingArguments(\n","    output_dir=output_dir,                 # directory to save and repository id\n","    per_device_train_batch_size=4,          # batch size per device during training\n","    gradient_accumulation_steps=4,          # number of steps before performing a backward/update pass\n","    gradient_checkpointing=True,            # use gradient checkpointing to save memory\n","    learning_rate=5e-4,                     # learning rate, based on QLoRA paper\n","    optim=\"paged_adamw_32bit\",              # use paged adamw optimizer\n","    logging_steps=10,                       # log every 10 steps\n","    max_steps = 50,                         # Number of training steps (overrides num_train_epochs)\n","    warmup_steps = 2,                       # Linear warmup steps from 0 to learning_rate\n","    fp16=True,\n","    bf16=False,\n","    #fp16 = False,                           # Enable fp16/bf16 training (set bf16 to True with an A100) # When you set True there'll be an error\n","    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper\n","    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper\n","    group_by_length=True,\n","    lr_scheduler_type=\"cosine\",               # use cosine learning rate scheduler\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:29.203656Z","iopub.status.busy":"2024-05-30T19:23:29.203279Z","iopub.status.idle":"2024-05-30T19:23:29.208765Z","shell.execute_reply":"2024-05-30T19:23:29.207832Z","shell.execute_reply.started":"2024-05-30T19:23:29.203626Z"},"id":"LvgjWenvJHFy"},"outputs":[],"source":["################################################################################\n","# QLoRA parameters\n","################################################################################\n","\n","# LoRA attention dimension\n","lora_r = 16\n","\n","# Alpha parameter for LoRA scaling\n","lora_alpha = 128\n","\n","# Dropout probability for LoRA layers\n","lora_dropout = 0.1\n","\n","# Bias\n","bias = \"none\"\n","\n","# Task type\n","task_type = \"CAUSAL_LM\""]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:31.180773Z","iopub.status.busy":"2024-05-30T19:23:31.180075Z","iopub.status.idle":"2024-05-30T19:23:31.189783Z","shell.execute_reply":"2024-05-30T19:23:31.188850Z","shell.execute_reply.started":"2024-05-30T19:23:31.180737Z"},"id":"y3okTO1dTs5x"},"outputs":[],"source":["def fine_tune(model,\n","          tokenizer,\n","          dataset,\n","          lora_r,\n","          lora_alpha,\n","          lora_dropout,\n","          bias,\n","          task_type,\n","          args):\n","    \"\"\"\n","    Prepares and fine-tune the pre-trained model.\n","\n","    :param model: Pre-trained Hugging Face model\n","    :param tokenizer: Model tokenizer\n","    :param dataset: Preprocessed training dataset\n","    \"\"\"\n","\n","    # Get LoRA module names\n","    target_modules = find_all_linear_names(model)\n","\n","    # Create PEFT configuration for these modules and wrap the model to PEFT\n","    peft_config = create_peft_config(lora_r, lora_alpha, target_modules, lora_dropout, bias, task_type)\n","\n","    # Print information about the percentage of trainable parameters\n","    print_trainable_parameters(model)\n","\n","    trainer = SFTTrainer(\n","        model=model,\n","        args=args,\n","        train_dataset=dataset,\n","        dataset_text_field=\"text\",\n","        peft_config=peft_config,\n","        tokenizer=tokenizer,\n","        max_seq_length=max_length,\n","        packing=True,\n","        dataset_kwargs={\n","            \"add_special_tokens\": False,\n","            \"append_concat_token\": False,\n","        }\n","    )\n","\n","    do_train = True\n","\n","    # Launch training and log metrics\n","    print(\"Training...\")\n","\n","    if do_train:\n","        train_result = trainer.train()\n","        metrics = train_result.metrics\n","        trainer.log_metrics(\"train\", metrics)\n","        trainer.save_metrics(\"train\", metrics)\n","        trainer.save_state()\n","        print(metrics)\n","\n","    # Save model\n","    print(\"Saving last checkpoint of the model...\")\n","    os.makedirs(output_dir, exist_ok = True)\n","    trainer.model.save_pretrained(output_dir)\n","    tokenizer.save_pretrained(output_dir)\n","\n","    # Free memory for merging weights\n","    del model\n","    del trainer\n","    torch.cuda.empty_cache()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":666,"referenced_widgets":["2ffaccd06a5d48139bb06b44cd222063","330e5901c4744d9491134c2c6e9ee6f8","b6246644dfa143a3930d5cfd626ac98e","24a0433def96430cb37c31318aad96e9","cb0073685b6f4d74ac7eca3678584440","cb126d1caf7f4268ab9bc9261578bc30","675a62ec65a84c9ea0449d1cb7ca69e9","9015525e13e04fa2a195f09a04c162fe","9cfa020214dd40cca435994777ecc779","af77905da4954e4bb8e2de0f19d49182","8d0299a5cebc4b6c846c7906076f5231"]},"execution":{"iopub.execute_input":"2024-05-23T15:12:16.749009Z","iopub.status.busy":"2024-05-23T15:12:16.748633Z","iopub.status.idle":"2024-05-23T15:13:02.307183Z","shell.execute_reply":"2024-05-23T15:13:02.305251Z","shell.execute_reply.started":"2024-05-23T15:12:16.748981Z"},"executionInfo":{"elapsed":617582,"status":"ok","timestamp":1724351249812,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"},"user_tz":-180},"id":"H_mbLzwATs9q","outputId":"0a9a2da0-6e17-4e5f-9955-944d83b08e22"},"outputs":[{"output_type":"stream","name":"stdout","text":["LoRA module names: ['v_proj', 'k_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj', 'q_proj']\n","All Parameters: 3,852,947,456 || Trainable Parameters: 363,286,528 || Trainable Parameters %: 9.428795283316731\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2ffaccd06a5d48139bb06b44cd222063"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:310: UserWarning: You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached.\n","  warnings.warn(\n","You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"output_type":"stream","name":"stdout","text":["Training...\n"]},{"output_type":"stream","name":"stderr","text":["`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='50' max='50' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [50/50 09:44, Epoch 1/2]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>10</td>\n","      <td>3.425600</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>2.646700</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>1.941700</td>\n","    </tr>\n","    <tr>\n","      <td>40</td>\n","      <td>1.573000</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>1.517700</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["***** train metrics *****\n","  epoch                    =       1.98\n","  total_flos               =  4116461GF\n","  train_loss               =     2.2209\n","  train_runtime            = 0:09:57.31\n","  train_samples_per_second =      1.339\n","  train_steps_per_second   =      0.084\n","{'train_runtime': 597.3105, 'train_samples_per_second': 1.339, 'train_steps_per_second': 0.084, 'total_flos': 4420016626728960.0, 'train_loss': 2.220939807891846, 'epoch': 1.98}\n","Saving last checkpoint of the model...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:141: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n","  warnings.warn(\n"]}],"source":["fine_tune(model,\n","      tokenizer,\n","      preprocessed_train,\n","      lora_r,\n","      lora_alpha,\n","      lora_dropout,\n","      bias,\n","      task_type,\n","      args)"]},{"cell_type":"code","source":["import gc\n","for _ in range(100):\n","    torch.cuda.empty_cache()\n","    gc.collect()"],"metadata":{"id":"PoldQvzFH5Zl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained(output_dir)\n","trained_model = AutoPeftModelForCausalLM.from_pretrained(\n","    output_dir,\n","    device_map=\"auto\",\n","    quantization_config=bnb_config\n",")\n","trained_model = trained_model.merge_and_unload()\n","trained_model"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":590,"referenced_widgets":["b367b1cf8afb4f269da353438830cf34","b7bcc36654a046bca719e0454ec3c0cc","86ee0e52e93e4876af66ad821d58812e","b5c6f8017f7b42e08605b226a5150c06","f02af8c1e10342f9a9b2032a0e610cad","f1b0e56a5e104e24860755a4f215116f","5800a3678a4946c9b84030dd99e9b258","4342815795f541f3bd46dfbe37543f79","337f82f7c32f4bf7b6c681073d9d3e84","38778b46a3e84f3899ab41ee71316bce","e86e7090c4304dfdbd0d28359dba6542"]},"id":"oIDiVWIqPGHi","executionInfo":{"status":"ok","timestamp":1724351436710,"user_tz":-180,"elapsed":120475,"user":{"displayName":"Clark Kent","userId":"04650579805289991680"}},"outputId":"2e0c31f3-3be1-44f8-80a0-55b904f29d1a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b367b1cf8afb4f269da353438830cf34"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n","/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:257: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["MistralForCausalLM(\n","  (model): MistralModel(\n","    (embed_tokens): Embedding(44314, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x MistralDecoderLayer(\n","        (self_attn): MistralAttention(\n","          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n","          (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n","          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (rotary_emb): MistralRotaryEmbedding()\n","        )\n","        (mlp): MistralMLP(\n","          (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n","          (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n","          (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n","          (act_fn): SiLU()\n","        )\n","        (input_layernorm): MistralRMSNorm()\n","        (post_attention_layernorm): MistralRMSNorm()\n","      )\n","    )\n","    (norm): MistralRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=44314, bias=False)\n",")"]},"metadata":{},"execution_count":64}]},{"cell_type":"markdown","metadata":{"id":"ZJ8jME1nLkJY"},"source":["### Testing the model without fine-tuning"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:32:19.402513Z","iopub.status.busy":"2024-05-30T20:32:19.401614Z","iopub.status.idle":"2024-05-30T20:32:19.407752Z","shell.execute_reply":"2024-05-30T20:32:19.406793Z","shell.execute_reply.started":"2024-05-30T20:32:19.402474Z"},"id":"zqdVvypPLkJZ"},"outputs":[],"source":["pipe = pipeline(task=\"text-generation\",\n","                model=model,\n","                tokenizer=tokenizer,\n","                max_new_tokens = 15,\n","                temperature = 0.0,\n","               )"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T15:43:30.574231Z","iopub.status.busy":"2024-05-30T15:43:30.573900Z","iopub.status.idle":"2024-05-30T15:43:34.814270Z","shell.execute_reply":"2024-05-30T15:43:34.813503Z","shell.execute_reply.started":"2024-05-30T15:43:30.574208Z"},"id":"A7CR7zpJLkJZ","outputId":"74921082-8e55-4c54-af49-b25fd09d1bc0"},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:389: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n","  warnings.warn(\n"]}],"source":["result = pipe(\"'Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\n### Talimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nGirdi:\\nuelkemdemuelteciistemiyorum uelkemdesuriyeliistemiyorum uelkemdesığınmacıistemiyorum ulkemdesurıyelııstemıyorum suriyelilersinirdisiedilsin suriyeliistemiyoruz suriyelilersehirlerdencıkartın yağmacılaravuremri askervepolisevuremri vuremriverilsin vuremriverilsin\\n\\n### Yanıt:'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T15:43:34.816053Z","iopub.status.busy":"2024-05-30T15:43:34.815764Z","iopub.status.idle":"2024-05-30T15:43:34.821879Z","shell.execute_reply":"2024-05-30T15:43:34.820947Z","shell.execute_reply.started":"2024-05-30T15:43:34.816028Z"},"id":"EVDcKTGKLkJZ","outputId":"bd941c9e-6bc8-44ab-f314-8144b862b0bb"},"outputs":[{"data":{"text/plain":["[{'generated_text': \"'Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\n### Talimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nGirdi:\\nuelkemdemuelteciistemiyorum uelkemdesuriyeliistemiyorum uelkemdesığınmacıistemiyorum ulkemdesurıyelııstemıyorum suriyelilersinirdisiedilsin suriyeliistemiyoruz suriyelilersehirlerdencıkartın yağmacılaravuremri askervepolisevuremri vuremriverilsin vuremriverilsin\\n\\n### Yanıt:'\\n\\nBu metin negatif bir tutum ve istekleri\"}]"]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["result"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T15:43:34.823102Z","iopub.status.busy":"2024-05-30T15:43:34.822842Z","iopub.status.idle":"2024-05-30T15:43:36.545094Z","shell.execute_reply":"2024-05-30T15:43:36.544313Z","shell.execute_reply.started":"2024-05-30T15:43:34.823079Z"},"id":"d1BPJgGBLkJZ"},"outputs":[],"source":["result2 = pipe(\"Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nMetin:\\nuser inşallah benim eski komşularım gittiler benim umudum kalmadı artık zor allah hala sağ olup enkazda olanlara sabır versin dışardakilere sabır versin vefat edenlere allah rahmet eylesinbabam c bloktaydı onu hanımını ilk gün suriyeliler çıkarmışlar\\n\\nYanıt:\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T15:43:36.547209Z","iopub.status.busy":"2024-05-30T15:43:36.546925Z","iopub.status.idle":"2024-05-30T15:43:36.552944Z","shell.execute_reply":"2024-05-30T15:43:36.552061Z","shell.execute_reply.started":"2024-05-30T15:43:36.547184Z"},"id":"_WAkGbo3JVDb","outputId":"7c850082-3fe6-4d71-daf1-4b466f4936d3"},"outputs":[{"data":{"text/plain":["[{'generated_text': 'Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nMetin:\\nuser inşallah benim eski komşularım gittiler benim umudum kalmadı artık zor allah hala sağ olup enkazda olanlara sabır versin dışardakilere sabır versin vefat edenlere allah rahmet eylesinbabam c bloktaydı onu hanımını ilk gün suriyeliler çıkarmışlar\\n\\nYanıt:\\nMetin, negatif bir tutum ve üzüntü'}]"]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["result2"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:40.766674Z","iopub.status.busy":"2024-05-30T19:23:40.765948Z","iopub.status.idle":"2024-05-30T19:23:40.772458Z","shell.execute_reply":"2024-05-30T19:23:40.771477Z","shell.execute_reply.started":"2024-05-30T19:23:40.766639Z"},"id":"cq4EsKq9LkJa","outputId":"9dab2a4d-a228-4d2a-a2ce-dc0d41eeb0df"},"outputs":[{"data":{"text/plain":["Dataset({\n","    features: ['instruction', 'input', 'label'],\n","    num_rows: 500\n","})"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["test"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:41.678178Z","iopub.status.busy":"2024-05-30T19:23:41.677295Z","iopub.status.idle":"2024-05-30T19:23:41.687104Z","shell.execute_reply":"2024-05-30T19:23:41.685916Z","shell.execute_reply.started":"2024-05-30T19:23:41.678138Z"},"id":"LPhZeBmULkJa","outputId":"ae5ece06-c544-437b-ae58-1c6c34c01e07"},"outputs":[{"data":{"text/plain":["{'instruction': ['Metni negatif veya pozitif olarak sınıflandır.',\n","  'Metni negatif veya pozitif olarak sınıflandır.'],\n"," 'input': ['username username username username kadın alaka soysuz yunan artığı ? kadınmısın ? kudurmuş sokak köpeği havlarsın işte böyle bana hoşt yunan it eniği demek düşer kendi başını',\n","  'keşke aynı kalabalık ay önce yunan abd ordusu ege ’ tam ortasında bulunan izmir açıklarındaki iskiri skiros adası ’ ortak tatbikat gerçekleştirip bunun adınada “ düşman ordusunun işgal ettiği adayı kurtarmak olduğu ” dediğinde olsaydı'],\n"," 'label': ['negatif', 'pozitif']}"]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["test[0:2]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:42.561401Z","iopub.status.busy":"2024-05-30T19:23:42.560842Z","iopub.status.idle":"2024-05-30T19:23:42.568771Z","shell.execute_reply":"2024-05-30T19:23:42.567835Z","shell.execute_reply.started":"2024-05-30T19:23:42.561362Z"},"id":"nie6zS1zLkJa","outputId":"ddcdceda-d9de-4b38-e947-d6ac31b93878"},"outputs":[{"data":{"text/plain":["{'instruction': ['Metni negatif veya pozitif olarak sınıflandır.',\n","  'Metni negatif veya pozitif olarak sınıflandır.'],\n"," 'input': ['username username username username kadın alaka soysuz yunan artığı ? kadınmısın ? kudurmuş sokak köpeği havlarsın işte böyle bana hoşt yunan it eniği demek düşer kendi başını',\n","  'keşke aynı kalabalık ay önce yunan abd ordusu ege ’ tam ortasında bulunan izmir açıklarındaki iskiri skiros adası ’ ortak tatbikat gerçekleştirip bunun adınada “ düşman ordusunun işgal ettiği adayı kurtarmak olduğu ” dediğinde olsaydı'],\n"," 'label': ['negatif', 'pozitif'],\n"," 'text': \"[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\n['Metni negatif veya pozitif olarak sınıflandır.', 'Metni negatif veya pozitif olarak sınıflandır.']\\n\\nMetin:\\n['username username username username kadın alaka soysuz yunan artığı ? kadınmısın ? kudurmuş sokak köpeği havlarsın işte böyle bana hoşt yunan it eniği demek düşer kendi başını', 'keşke aynı kalabalık ay önce yunan abd ordusu ege ’ tam ortasında bulunan izmir açıklarındaki iskiri skiros adası ’ ortak tatbikat gerçekleştirip bunun adınada “ düşman ordusunun işgal ettiği adayı kurtarmak olduğu ” dediğinde olsaydı']\\n\\nYanıt:\"}"]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["prompt_test_data = create_prompt_test_formats(test[0:2])\n","prompt_test_data"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"execution":{"iopub.execute_input":"2024-05-30T19:23:49.838435Z","iopub.status.busy":"2024-05-30T19:23:49.837824Z","iopub.status.idle":"2024-05-30T19:23:49.857870Z","shell.execute_reply":"2024-05-30T19:23:49.857038Z","shell.execute_reply.started":"2024-05-30T19:23:49.838404Z"},"jupyter":{"outputs_hidden":true},"id":"x0GT3i2FLkJb","outputId":"03482cac-49ce-4a8f-e5db-46f56134938f"},"outputs":[{"data":{"text/plain":["['negatif',\n"," 'pozitif',\n"," 'negatif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'negatif',\n"," 'pozitif',\n"," 'negatif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'negatif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 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test[\"label\"]\n","y_true"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:55.127016Z","iopub.status.busy":"2024-05-30T19:23:55.126654Z","iopub.status.idle":"2024-05-30T19:23:55.222299Z","shell.execute_reply":"2024-05-30T19:23:55.221354Z","shell.execute_reply.started":"2024-05-30T19:23:55.126986Z"},"colab":{"referenced_widgets":["bada5ceb40474f389d01b68b0e76477f"]},"id":"I6eM38q6LkJb","outputId":"7f23ddc3-7586-4dde-bb71-268f90a68140"},"outputs":[{"name":"stdout","output_type":"stream","text":["Preprocessing dataset...\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bada5ceb40474f389d01b68b0e76477f","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/500 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["preprocessed_test = preprocess_test_dataset(test)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:56.387984Z","iopub.status.busy":"2024-05-30T19:23:56.387624Z","iopub.status.idle":"2024-05-30T19:23:56.393995Z","shell.execute_reply":"2024-05-30T19:23:56.393274Z","shell.execute_reply.started":"2024-05-30T19:23:56.387959Z"},"id":"NXP4EzaWLkJb","outputId":"d42d346d-200d-4fb4-85c5-78469be145e6"},"outputs":[{"data":{"text/plain":["Dataset({\n","    features: ['text'],\n","    num_rows: 500\n","})"]},"execution_count":63,"metadata":{},"output_type":"execute_result"}],"source":["preprocessed_test"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:57.145068Z","iopub.status.busy":"2024-05-30T19:23:57.144367Z","iopub.status.idle":"2024-05-30T19:23:57.150863Z","shell.execute_reply":"2024-05-30T19:23:57.149817Z","shell.execute_reply.started":"2024-05-30T19:23:57.145038Z"},"id":"AoK5rsyzLkJb","outputId":"68a5624d-fa22-468e-ac59-6c4eeed2bb24"},"outputs":[{"data":{"text/plain":["{'text': '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nMetni negatif veya pozitif olarak sınıflandır.\\n\\nMetin:\\nııllcxlj sqedere un bbcword terrorist israel gazzedekatliamvar\\n\\nYanıt:'}"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["preprocessed_test[22]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:23:57.764217Z","iopub.status.busy":"2024-05-30T19:23:57.763902Z","iopub.status.idle":"2024-05-30T19:23:57.771219Z","shell.execute_reply":"2024-05-30T19:23:57.770361Z","shell.execute_reply.started":"2024-05-30T19:23:57.764191Z"},"id":"RNV4HjQsLkJc"},"outputs":[],"source":["def predict(data, model, tokenizer):\n","    y_pred = []\n","\n","    for i in range(len(data)):\n","        prompt = data[i][\"text\"]\n","\n","        pipe = pipeline(task=\"text-generation\",\n","                        model=model,\n","                        tokenizer=tokenizer,\n","                        max_new_tokens = 10,\n","                        temperature = 0.0,\n","                       )\n","\n","        result = pipe(prompt)\n","\n","        answer = result[0]['generated_text'].split(\":\")[-1]\n","        #print(answer)\n","\n","        if \"pozitif\" in answer:\n","            y_pred.append(\"negatif\")   # 0 - negatif\n","        elif \"negatif\" in answer:\n","            y_pred.append(\"pozitif\")   # 1 - pozitif\n","        else:\n","            y_pred.append(\"none\")\n","\n","    return y_pred"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"execution":{"iopub.execute_input":"2024-05-30T19:23:58.694127Z","iopub.status.busy":"2024-05-30T19:23:58.693472Z","iopub.status.idle":"2024-05-30T19:38:37.607414Z","shell.execute_reply":"2024-05-30T19:38:37.606477Z","shell.execute_reply.started":"2024-05-30T19:23:58.694098Z"},"jupyter":{"outputs_hidden":true},"id":"FlmUHse_LkJc","outputId":"496ffa09-7a24-4a31-ea76-889ec3d7a490"},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:389: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n","  warnings.warn(\n"]},{"data":{"text/plain":["['pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'negatif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'none',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 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'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'none',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'none',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif',\n"," 'pozitif']"]},"execution_count":66,"metadata":{},"output_type":"execute_result"}],"source":["y_pred = predict(preprocessed_test, model, tokenizer)\n","y_pred"]},{"cell_type":"markdown","metadata":{"id":"XgvRJCRXLkJc"},"source":["#### Changing all \"none\" values to \"negatif\" values"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:38:56.050124Z","iopub.status.busy":"2024-05-30T19:38:56.049502Z","iopub.status.idle":"2024-05-30T19:38:56.056595Z","shell.execute_reply":"2024-05-30T19:38:56.055593Z","shell.execute_reply.started":"2024-05-30T19:38:56.050094Z"},"id":"3ObeiWL1LkJc","outputId":"36408c08-9cde-44c1-b63d-13e18e5ebd8a"},"outputs":[{"data":{"text/plain":["4"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["y_pred.count(\"none\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:38:57.074002Z","iopub.status.busy":"2024-05-30T19:38:57.073666Z","iopub.status.idle":"2024-05-30T19:38:57.079493Z","shell.execute_reply":"2024-05-30T19:38:57.078598Z","shell.execute_reply.started":"2024-05-30T19:38:57.073977Z"},"id":"egUXbPiXLkJd","outputId":"dbc08d26-d3b2-48a1-a9b9-2c60dd4de2b1"},"outputs":[{"name":"stdout","output_type":"stream","text":["0\n"]}],"source":["y_pred_copy = y_pred.copy()\n","y_pred_copy = [p.replace(\"none\", \"negatif\") for p in y_pred_copy]\n","\n","print(y_pred_copy.count(\"none\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:38:57.943490Z","iopub.status.busy":"2024-05-30T19:38:57.942941Z","iopub.status.idle":"2024-05-30T19:38:57.949733Z","shell.execute_reply":"2024-05-30T19:38:57.948889Z","shell.execute_reply.started":"2024-05-30T19:38:57.943463Z"},"id":"8t6nD109LkJd","outputId":"9c37c225-8778-45fa-f243-a7198e351e45"},"outputs":[{"data":{"text/plain":["['negatif', 'pozitif', 'negatif', 'pozitif', 'pozitif', 'pozitif', 'pozitif']"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":["test[0:7][\"label\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:38:59.747280Z","iopub.status.busy":"2024-05-30T19:38:59.746934Z","iopub.status.idle":"2024-05-30T19:38:59.753191Z","shell.execute_reply":"2024-05-30T19:38:59.752372Z","shell.execute_reply.started":"2024-05-30T19:38:59.747250Z"},"id":"s7utTdzuLkJd","outputId":"f70e58fe-1e17-4b7c-ee69-0b20b2639f5b"},"outputs":[{"data":{"text/plain":["['pozitif', 'pozitif', 'pozitif', 'pozitif', 'pozitif', 'pozitif', 'pozitif']"]},"execution_count":70,"metadata":{},"output_type":"execute_result"}],"source":["y_pred[0:7]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:39:00.553591Z","iopub.status.busy":"2024-05-30T19:39:00.552937Z","iopub.status.idle":"2024-05-30T19:39:00.865970Z","shell.execute_reply":"2024-05-30T19:39:00.865086Z","shell.execute_reply.started":"2024-05-30T19:39:00.553537Z"},"id":"p7sW_EdkLkJd","outputId":"648160b6-2f04-404b-bc73-bd7c13c64c09"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["label_map = {\n","    'LABEL_0': 'Non-Hate',\n","    'LABEL_1': 'Hate',\n","}\n","\n","cm = confusion_matrix(y_true, y_pred_copy)\n","\n","# label_map to labels\n","labels = [label_map[f'LABEL_{i}'] for i in range(len(label_map))]\n","\n","# Confusion Matrix\n","sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n","plt.xlabel('Predicted labels')\n","plt.ylabel('True labels')\n","plt.title('Confusion Matrix with Label Names')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:39:01.344486Z","iopub.status.busy":"2024-05-30T19:39:01.344166Z","iopub.status.idle":"2024-05-30T19:39:01.363822Z","shell.execute_reply":"2024-05-30T19:39:01.362913Z","shell.execute_reply.started":"2024-05-30T19:39:01.344459Z"},"id":"6n0I34H7LkJe","outputId":"18e6d939-e939-4cc6-8837-c42f9274f79f"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","     negatif       0.17      0.01      0.02       157\n","     pozitif       0.68      0.97      0.80       343\n","\n","    accuracy                           0.67       500\n","   macro avg       0.42      0.49      0.41       500\n","weighted avg       0.52      0.67      0.56       500\n","\n"]}],"source":["print(classification_report(y_true, y_pred_copy))"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:39:02.485403Z","iopub.status.busy":"2024-05-30T19:39:02.485082Z","iopub.status.idle":"2024-05-30T19:39:02.496948Z","shell.execute_reply":"2024-05-30T19:39:02.495992Z","shell.execute_reply.started":"2024-05-30T19:39:02.485375Z"},"id":"RTiSStUNLkJe","outputId":"b3fa681c-2fa0-4e10-e585-f7b244ae5e5b"},"outputs":[{"data":{"text/plain":["0.412556341187277"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["f1_score(y_true, y_pred_copy, average='macro')"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:39:02.942635Z","iopub.status.busy":"2024-05-30T19:39:02.941982Z","iopub.status.idle":"2024-05-30T19:39:02.955428Z","shell.execute_reply":"2024-05-30T19:39:02.954407Z","shell.execute_reply.started":"2024-05-30T19:39:02.942592Z"},"id":"jFWy8b6RLkJe","outputId":"9e75dc21-32e2-46cb-ea52-b2a00762f420"},"outputs":[{"data":{"text/plain":["0.67"]},"execution_count":74,"metadata":{},"output_type":"execute_result"}],"source":["f1_score(y_true, y_pred_copy, average='micro')"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:39:04.125277Z","iopub.status.busy":"2024-05-30T19:39:04.124433Z","iopub.status.idle":"2024-05-30T19:39:04.138059Z","shell.execute_reply":"2024-05-30T19:39:04.137107Z","shell.execute_reply.started":"2024-05-30T19:39:04.125243Z"},"id":"KTqvpAVhLkJf","outputId":"b610ae11-4b57-4c7a-a3ab-d075683a0ef8"},"outputs":[{"data":{"text/plain":["0.5572225663811333"]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":["f1_score(y_true, y_pred_copy, average='weighted')"]},{"cell_type":"markdown","metadata":{"id":"MYgM77i4LkJf"},"source":["### Testing the model with new prompt (Hate Definition)"]},{"cell_type":"markdown","metadata":{"id":"2p13tELMLkJf"},"source":["**The Definition:** Bu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:40:01.029503Z","iopub.status.busy":"2024-05-30T19:40:01.028694Z","iopub.status.idle":"2024-05-30T19:40:01.035486Z","shell.execute_reply":"2024-05-30T19:40:01.034521Z","shell.execute_reply.started":"2024-05-30T19:40:01.029471Z"},"id":"EGma-XT_LkJf","outputId":"99e76745-793b-4963-bff3-fe5735483099"},"outputs":[{"data":{"text/plain":["Dataset({\n","    features: ['instruction', 'input', 'label'],\n","    num_rows: 500\n","})"]},"execution_count":76,"metadata":{},"output_type":"execute_result"}],"source":["test"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:40:01.808257Z","iopub.status.busy":"2024-05-30T19:40:01.807736Z","iopub.status.idle":"2024-05-30T19:40:01.818366Z","shell.execute_reply":"2024-05-30T19:40:01.817582Z","shell.execute_reply.started":"2024-05-30T19:40:01.808229Z"},"id":"516gDB6NLkJf","outputId":"d6335aad-9788-4bd1-f896-946e178b68dd"},"outputs":[{"data":{"text/plain":["['Metni negatif veya pozitif olarak sınıflandır.',\n"," 'Metni negatif veya pozitif olarak sınıflandır.',\n"," 'Metni negatif veya pozitif olarak sınıflandır.',\n"," 'Metni negatif veya pozitif olarak sınıflandır.',\n"," 'Metni negatif veya pozitif olarak sınıflandır.']"]},"execution_count":77,"metadata":{},"output_type":"execute_result"}],"source":["test[\"instruction\"][0:5]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:58:22.310733Z","iopub.status.busy":"2024-05-30T19:58:22.309787Z","iopub.status.idle":"2024-05-30T19:58:22.315459Z","shell.execute_reply":"2024-05-30T19:58:22.314523Z","shell.execute_reply.started":"2024-05-30T19:58:22.310695Z"},"id":"BGriW4kQLkJg"},"outputs":[],"source":["def change_instruction(sample):\n","\n","    # Initialize static strings for the prompt template\n","    new = \"Bu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\"\n","\n","    # Store the formatted prompt template in a new key \"text\"\n","    sample[\"instruction\"] = new\n","\n","    return sample"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:58:23.102666Z","iopub.status.busy":"2024-05-30T19:58:23.102054Z","iopub.status.idle":"2024-05-30T19:58:23.107071Z","shell.execute_reply":"2024-05-30T19:58:23.106187Z","shell.execute_reply.started":"2024-05-30T19:58:23.102633Z"},"id":"OTigQQxXLkJg"},"outputs":[],"source":["def preprocess_for_instruction(dataset: str):\n","\n","    # Add prompt to each sample\n","    print(\"Preprocessing dataset...\")\n","    dataset = dataset.map(change_instruction)\n","\n","    return dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:58:24.198502Z","iopub.status.busy":"2024-05-30T19:58:24.197922Z","iopub.status.idle":"2024-05-30T19:58:24.269737Z","shell.execute_reply":"2024-05-30T19:58:24.268821Z","shell.execute_reply.started":"2024-05-30T19:58:24.198467Z"},"colab":{"referenced_widgets":["67575d9b0508461aa4bf49c42b99f2e3"]},"id":"VuGaRs-ELkJg","outputId":"5a1aa55f-bdf6-4b2e-87f6-d2865efccd87"},"outputs":[{"name":"stdout","output_type":"stream","text":["Preprocessing dataset...\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"67575d9b0508461aa4bf49c42b99f2e3","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/500 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["new_test = preprocess_for_instruction(test)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T19:59:05.895428Z","iopub.status.busy":"2024-05-30T19:59:05.894810Z","iopub.status.idle":"2024-05-30T19:59:05.903529Z","shell.execute_reply":"2024-05-30T19:59:05.902709Z","shell.execute_reply.started":"2024-05-30T19:59:05.895396Z"},"id":"u-zQbx5HLkJh","outputId":"6c34ef8f-b7ea-4f81-8bf6-2a709bc4db36"},"outputs":[{"data":{"text/plain":["['Bu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.',\n"," 'Bu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.',\n"," 'Bu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.',\n"," 'Bu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.',\n"," 'Bu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.']"]},"execution_count":86,"metadata":{},"output_type":"execute_result"}],"source":["new_test[\"instruction\"][0:5]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:00:21.694629Z","iopub.status.busy":"2024-05-30T20:00:21.693958Z","iopub.status.idle":"2024-05-30T20:00:21.772730Z","shell.execute_reply":"2024-05-30T20:00:21.771857Z","shell.execute_reply.started":"2024-05-30T20:00:21.694584Z"},"colab":{"referenced_widgets":["d239d089f9ac469e8175ca90538e2598"]},"id":"0yOiZjMTLkJh","outputId":"278e63e5-c861-4a08-8d52-4ff3b54f3800"},"outputs":[{"name":"stdout","output_type":"stream","text":["Preprocessing dataset...\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d239d089f9ac469e8175ca90538e2598","version_major":2,"version_minor":0},"text/plain":["Map:   0%|          | 0/500 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["preprocessed_test = preprocess_test_dataset(new_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:26:24.739284Z","iopub.status.busy":"2024-05-30T20:26:24.738949Z","iopub.status.idle":"2024-05-30T20:26:24.745872Z","shell.execute_reply":"2024-05-30T20:26:24.744867Z","shell.execute_reply.started":"2024-05-30T20:26:24.739259Z"},"id":"HK3zQ803LkJh","outputId":"c0cfe683-90e8-43ce-fd7b-0cbc6b305c87"},"outputs":[{"data":{"text/plain":["{'instruction': ['Metni negatif veya pozitif olarak sınıflandır.',\n","  'Metni negatif veya pozitif olarak sınıflandır.'],\n"," 'input': ['username username username username kadın alaka soysuz yunan artığı ? kadınmısın ? kudurmuş sokak köpeği havlarsın işte böyle bana hoşt yunan it eniği demek düşer kendi başını',\n","  'keşke aynı kalabalık ay önce yunan abd ordusu ege ’ tam ortasında bulunan izmir açıklarındaki iskiri skiros adası ’ ortak tatbikat gerçekleştirip bunun adınada “ düşman ordusunun işgal ettiği adayı kurtarmak olduğu ” dediğinde olsaydı'],\n"," 'label': ['negatif', 'pozitif']}"]},"execution_count":98,"metadata":{},"output_type":"execute_result"}],"source":["test[0:2]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:27:50.086221Z","iopub.status.busy":"2024-05-30T20:27:50.085307Z","iopub.status.idle":"2024-05-30T20:27:50.092484Z","shell.execute_reply":"2024-05-30T20:27:50.091544Z","shell.execute_reply.started":"2024-05-30T20:27:50.086183Z"},"id":"5syNd-SXLkJh","outputId":"49234683-b7b0-4549-a421-58d8e9c6ea8b"},"outputs":[{"data":{"text/plain":["{'text': ['[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\nuser uğur kardaş yaa tek hücrelinin ismini öğrenmekle uğraşıcaz hızlı artıyolar \\u200d♀️\\n\\nYanıt:',\n","  '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\ndemişiz temmuz depremi ilgili olarakturkeyquakesuriyeliler yimpaşturquia dulkadiroğluhelal yunanistan uçak\\n\\nYanıt:',\n","  '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\ngözaltına alınan uğur kardaş afgan uyruklu kişinin cesetlerin kolunu keserek altın aldığına ilişkin olaya şahit olmadım ancak şehirde tarz iddialar konuşuluyordu demiş yapmayın\\n\\nYanıt:']}"]},"execution_count":102,"metadata":{},"output_type":"execute_result"}],"source":["preprocessed_test[2:5]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:33:03.307915Z","iopub.status.busy":"2024-05-30T20:33:03.307088Z","iopub.status.idle":"2024-05-30T20:33:06.000691Z","shell.execute_reply":"2024-05-30T20:33:05.999702Z","shell.execute_reply.started":"2024-05-30T20:33:03.307880Z"},"id":"VlMW7PWoLkJh"},"outputs":[],"source":["result3 = pipe(\"'[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\nusername username username username kadın alaka soysuz yunan artığı ? kadınmısın ? kudurmuş sokak köpeği havlarsın işte böyle bana hoşt yunan it eniği demek düşer kendi başını\\n\\nYanıt:\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:33:06.002522Z","iopub.status.busy":"2024-05-30T20:33:06.002212Z","iopub.status.idle":"2024-05-30T20:33:06.008587Z","shell.execute_reply":"2024-05-30T20:33:06.007501Z","shell.execute_reply.started":"2024-05-30T20:33:06.002492Z"},"id":"RRyZ0M9XLkJi","outputId":"6ff57775-4edc-448c-8e7d-762be7c9322f"},"outputs":[{"data":{"text/plain":["[{'generated_text': \"'[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\nusername username username username kadın alaka soysuz yunan artığı ? kadınmısın ? kudurmuş sokak köpeği havlarsın işte böyle bana hoşt yunan it eniği demek düşer kendi başını\\n\\nYanıt:\\nBu metin nefret içerikli bir anlamı içermektedir. Pozitif yahut negatif\"}]"]},"execution_count":117,"metadata":{},"output_type":"execute_result"}],"source":["result3"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:32:52.128015Z","iopub.status.busy":"2024-05-30T20:32:52.127649Z","iopub.status.idle":"2024-05-30T20:32:54.801276Z","shell.execute_reply":"2024-05-30T20:32:54.800510Z","shell.execute_reply.started":"2024-05-30T20:32:52.127986Z"},"id":"AH6ncDG-LkJi"},"outputs":[],"source":["result4 = pipe('[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\nkeşke aynı kalabalık ay önce yunan abd ordusu ege ’ tam ortasında bulunan izmir açıklarındaki iskiri skiros adası ’ ortak tatbikat gerçekleştirip bunun adınada “ düşman ordusunun işgal ettiği adayı kurtarmak olduğu ” dediğinde olsaydı\\n\\nYanıt:')"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:32:54.802975Z","iopub.status.busy":"2024-05-30T20:32:54.802688Z","iopub.status.idle":"2024-05-30T20:32:54.808620Z","shell.execute_reply":"2024-05-30T20:32:54.807617Z","shell.execute_reply.started":"2024-05-30T20:32:54.802951Z"},"id":"qBRkk0Q6LkJi","outputId":"11b8527b-fa00-4df3-b80a-03d619fd32ed"},"outputs":[{"data":{"text/plain":["[{'generated_text': '[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\nkeşke aynı kalabalık ay önce yunan abd ordusu ege ’ tam ortasında bulunan izmir açıklarındaki iskiri skiros adası ’ ortak tatbikat gerçekleştirip bunun adınada “ düşman ordusunun işgal ettiği adayı kurtarmak olduğu ” dediğinde olsaydı\\n\\nYanıt:\\nBu metin nefret içerikli bir anlamı içermemektedir. Metin, bir'}]"]},"execution_count":115,"metadata":{},"output_type":"execute_result"}],"source":["result4"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:33:36.595300Z","iopub.status.busy":"2024-05-30T20:33:36.594437Z","iopub.status.idle":"2024-05-30T20:33:39.325639Z","shell.execute_reply":"2024-05-30T20:33:39.324812Z","shell.execute_reply.started":"2024-05-30T20:33:36.595265Z"},"id":"Ydnfy1AJLkJi"},"outputs":[],"source":["result5 = pipe(\"'[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\ngözaltına alınan uğur kardaş afgan uyruklu kişinin cesetlerin kolunu keserek altın aldığına ilişkin olaya şahit olmadım ancak şehirde tarz iddialar konuşuluyordu demiş yapmayın\\n\\nYanıt:'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:33:39.327312Z","iopub.status.busy":"2024-05-30T20:33:39.327032Z","iopub.status.idle":"2024-05-30T20:33:39.333800Z","shell.execute_reply":"2024-05-30T20:33:39.332785Z","shell.execute_reply.started":"2024-05-30T20:33:39.327286Z"},"id":"FdQItkL2LkJj","outputId":"6b50b6c6-fb8a-4953-cdd7-93c3f9c22cbf"},"outputs":[{"data":{"text/plain":["[{'generated_text': \"'[INST] Aşağıda bir görevi açıklayan bir talimat bulunmaktadır. Talimatı uygun şekilde tamamlayan bir yanıt yazın.\\n\\nTalimat:\\nBu metnin nefret içerikli bir anlamı var mı? Varsa pozitif yoksa negatif şeklinde sınıflandır. Not: Bir metin nefret içeriyorsa veya tehdit, kişisel saldırı, iftira veya istismar gibi metinsel nefret içeriyorsa nefret içerikli olarak sınıflandırılır. Bir metin, olayları bildiriyorsa veya başkalarının görüşlerini nefret içermeyen bir şekilde objektif olarak bildiriyorsa nefret içermeyen olarak kabul edilir.\\n\\nMetin:\\ngözaltına alınan uğur kardaş afgan uyruklu kişinin cesetlerin kolunu keserek altın aldığına ilişkin olaya şahit olmadım ancak şehirde tarz iddialar konuşuluyordu demiş yapmayın\\n\\nYanıt:'':\\nBu metin nefret içerikli bir anlamı içermemektedir. Metin,\"}]"]},"execution_count":119,"metadata":{},"output_type":"execute_result"}],"source":["result5"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:34:19.834125Z","iopub.status.busy":"2024-05-30T20:34:19.833487Z","iopub.status.idle":"2024-05-30T20:34:19.841119Z","shell.execute_reply":"2024-05-30T20:34:19.840100Z","shell.execute_reply.started":"2024-05-30T20:34:19.834092Z"},"id":"GFrMY9FWLkJj"},"outputs":[],"source":["def new_predict(data, model, tokenizer):\n","    y_pred = []\n","\n","    for i in range(len(data)):\n","        prompt = data[i][\"text\"]\n","\n","        pipe = pipeline(task=\"text-generation\",\n","                        model=model,\n","                        tokenizer=tokenizer,\n","                        max_new_tokens = 15,\n","                        temperature = 0.0,\n","                       )\n","\n","        result = pipe(prompt)\n","\n","        answer = result[0]['generated_text'].split(\":\")[-1]\n","        #print(answer)\n","\n","        if \"içermemektedir\" in answer:\n","            y_pred.append(\"negatif\")   # 0 - negatif\n","        elif \"içermektedir\" in answer:\n","            y_pred.append(\"pozitif\")   # 1 - pozitif\n","        else:\n","            y_pred.append(\"none\")\n","\n","    return y_pred"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"execution":{"iopub.execute_input":"2024-05-30T20:34:20.528801Z","iopub.status.busy":"2024-05-30T20:34:20.528444Z","iopub.status.idle":"2024-05-30T20:55:21.719723Z","shell.execute_reply":"2024-05-30T20:55:21.718796Z","shell.execute_reply.started":"2024-05-30T20:34:20.528774Z"},"jupyter":{"outputs_hidden":true},"id":"kx3dzFqRLkJj","outputId":"dae1df9c-a56f-4b98-e148-b8d150e8e70c"},"outputs":[{"data":{"text/plain":["['pozitif',\n"," 'negatif',\n"," 'negatif',\n"," 'negatif',\n"," 'negatif',\n"," 'pozitif',\n"," 'none',\n"," 'negatif',\n"," 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new_predict(preprocessed_test, model, tokenizer)\n","y_pred"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:56:12.993718Z","iopub.status.busy":"2024-05-30T20:56:12.993349Z","iopub.status.idle":"2024-05-30T20:56:12.999502Z","shell.execute_reply":"2024-05-30T20:56:12.998600Z","shell.execute_reply.started":"2024-05-30T20:56:12.993688Z"},"id":"4jTjCbw2LkJj","outputId":"f089199b-49b3-4e89-e38a-1a42d97a13e4"},"outputs":[{"data":{"text/plain":["14"]},"execution_count":122,"metadata":{},"output_type":"execute_result"}],"source":["y_pred.count(\"none\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:56:46.564165Z","iopub.status.busy":"2024-05-30T20:56:46.563808Z","iopub.status.idle":"2024-05-30T20:56:46.569762Z","shell.execute_reply":"2024-05-30T20:56:46.568791Z","shell.execute_reply.started":"2024-05-30T20:56:46.564133Z"},"id":"t9AX9rrvLkJk","outputId":"e6d894b6-a88a-41fc-c764-9266134b48d1"},"outputs":[{"name":"stdout","output_type":"stream","text":["0\n"]}],"source":["y_pred_copy = y_pred.copy()\n","y_pred_copy = [p.replace(\"none\", \"negatif\") for p in y_pred_copy]\n","\n","print(y_pred_copy.count(\"none\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:57:16.542083Z","iopub.status.busy":"2024-05-30T20:57:16.541698Z","iopub.status.idle":"2024-05-30T20:57:16.833687Z","shell.execute_reply":"2024-05-30T20:57:16.832686Z","shell.execute_reply.started":"2024-05-30T20:57:16.542047Z"},"id":"IVkVW7ZjLkJk","outputId":"66f569fe-c362-4f59-8632-19ac3432b263"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["label_map = {\n","    'LABEL_0': 'Non-Hate',\n","    'LABEL_1': 'Hate',\n","}\n","\n","cm = confusion_matrix(y_true, y_pred_copy)\n","\n","# label_map to labels\n","labels = [label_map[f'LABEL_{i}'] for i in range(len(label_map))]\n","\n","# Confusion Matrix\n","sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n","plt.xlabel('Predicted labels')\n","plt.ylabel('True labels')\n","plt.title('Confusion Matrix with Label Names')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-05-30T20:57:39.639362Z","iopub.status.busy":"2024-05-30T20:57:39.638541Z","iopub.status.idle":"2024-05-30T20:57:39.659197Z","shell.execute_reply":"2024-05-30T20:57:39.658300Z","shell.execute_reply.started":"2024-05-30T20:57:39.639331Z"},"id":"dYIgg0IuLkJk","outputId":"59742450-dac8-42ed-e458-f2d938d7b9e0"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","     negatif       0.18      0.25      0.21       157\n","     pozitif       0.58      0.48      0.53       343\n","\n","    accuracy                           0.41       500\n","   macro avg       0.38      0.36      0.37       500\n","weighted avg       0.46      0.41      0.43       500\n","\n"]}],"source":["print(classification_report(y_true, 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