Add Continuous Integration script

.github/workflows/check_build.yml

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+# Checks if the build works, 
+# by installing the requirements
+# and then running the example code
+
+name: Check build
+
+on:
+  push:
+    branches:
+      - main
+jobs:
+  check_build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: install required packages
+        run: python3 -m pip install -r requirements.txt
+
+      - name: run example code shown in README
+        run: python3 .github/workflows/example_huggingface_v4_28.py

.github/workflows/example_huggingface_newer_than_v4_28.py

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+from transformers.models.bert.configuration_bert import BertConfig
+
+config = BertConfig.from_pretrained("zhihan1996/DNABERT-2-117M")
+model = AutoModel.from_pretrained("zhihan1996/DNABERT-2-117M", trust_remote_code=True, config=config)
+
+dna = "ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC"
+inputs = tokenizer(dna, return_tensors = 'pt')["input_ids"]
+hidden_states = model(inputs)[0] # [1, sequence_length, 768]
+
+# embedding with mean pooling
+embedding_mean = torch.mean(hidden_states[0], dim=0)
+print(embedding_mean.shape) # expect to be 768
+
+# embedding with max pooling
+embedding_max = torch.max(hidden_states[0], dim=0)[0]
+print(embedding_max.shape) # expect to be 768

.github/workflows/example_huggingface_v4_28.py

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+import torch
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("zhihan1996/DNABERT-2-117M", trust_remote_code=True)
+model = AutoModel.from_pretrained("zhihan1996/DNABERT-2-117M", trust_remote_code=True)
+
+dna = "ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC"
+inputs = tokenizer(dna, return_tensors = 'pt')["input_ids"]
+hidden_states = model(inputs)[0] # [1, sequence_length, 768]
+
+# embedding with mean pooling
+embedding_mean = torch.mean(hidden_states[0], dim=0)
+print(embedding_mean.shape) # expect to be 768
+
+# embedding with max pooling
+embedding_max = torch.max(hidden_states[0], dim=0)[0]
+print(embedding_max.shape) # expect to be 768