Update pytorch-partial-tagger (#34)

* Bump pytorch-partial-tagger version

* Update incompatible parts

* Bump version

* Fix the import order in tokenizer.py

* Update requirements.txt

pyproject.toml

@@ -8,7 +8,7 @@ requires-python = ">=3.8"
 
 [tool.poetry]
 name = "spacy-partial-tagger"
-version = "0.13.0"
+version = "0.14.0"
 description = "Sequence Tagger for Partially Annotated Dataset in spaCy"
 authors = ["yasufumi <yasufumi.taniguchi@gmail.com>"]
 license = "MIT"
@@ -27,7 +27,7 @@ transformers = {extras = ["ja"], version = "^4.25.1"}
 torch = "^2.0.1"
 spacy = {extras = ["transformers"], version = "^3.3.1"}
 spacy-alignments = "^0.8.5"
-pytorch-partial-tagger = "^0.1.5"
+pytorch-partial-tagger = "^0.1.6"
 
 [tool.poetry.group.dev.dependencies]
 mypy = "^1.3.0"

requirements.txt

@@ -40,9 +40,9 @@ pydantic==1.10.8 ; python_version >= "3.8" and python_version < "4.0"
 pyflakes==2.4.0 ; python_version >= "3.8" and python_version < "4.0"
 pytest-cov==3.0.0 ; python_version >= "3.8" and python_version < "4.0"
 pytest==7.3.1 ; python_version >= "3.8" and python_version < "4.0"
-pytorch-partial-tagger==0.1.5 ; python_version >= "3.8" and python_version < "4.0"
+pytorch-partial-tagger==0.1.6 ; python_version >= "3.8" and python_version < "4.0"
 pyyaml==6.0 ; python_version >= "3.8" and python_version < "4.0"
-regex==2023.5.5 ; python_version >= "3.8" and python_version < "4.0"
+regex==2023.6.3 ; python_version >= "3.8" and python_version < "4.0"
 requests==2.31.0 ; python_version >= "3.8" and python_version < "4.0"
 rhoknp==1.3.1 ; python_version >= "3.8" and python_version < "4.0"
 ruff==0.0.270 ; python_version >= "3.8" and python_version < "4.0"

spacy_partial_tagger/pipeline.py

@@ -52,9 +52,9 @@ def set_annotations(
         tokenized_texts = [doc.user_data["tokenized_text"] for doc in docs]
         tag_factory = TagFactory(tokenized_texts, self.label_set)
 
-        tags_collection = tag_factory.create_char_based_tags(tag_indices)
+        tags_batch = tag_factory.create_char_based_tags(tag_indices)
 
-        for doc, tags in zip(docs, tags_collection):
+        for doc, tags in zip(docs, tags_batch):
             ents = []
             for tag in tags:
                 span = doc.char_span(tag.start, tag.start + tag.length, tag.label)
@@ -115,13 +115,13 @@ def get_loss(
         ]
         tag_factory = TagFactory(tokenized_texts, self.label_set)
 
-        tags_collection = []
+        tags_batch = []
         for example in examples:
             tags = tuple(
                 create_tag(ent.start_char, len(ent.text), ent.label_)
                 for ent in example.y.ents
             )
-            tags_collection.append(CharBasedTags(tags, example.y.text))
+            tags_batch.append(CharBasedTags(tags, example.y.text))
 
         lengths = [text.num_tokens for text in tokenized_texts]
         max_length = max(lengths)
@@ -130,9 +130,7 @@ def get_loss(
             device=scores_pt.device,
         )
 
-        tag_bitmap = tag_factory.create_tag_bitmap(
-            tuple(tags_collection), scores_pt.device
-        )
+        tag_bitmap = tag_factory.create_tag_bitmap(tuple(tags_batch), scores_pt.device)
 
         loss = expected_entity_ratio_loss(
             scores_pt, tag_bitmap, mask, self.label_set.get_outside_index()

spacy_partial_tagger/tagger.py

@@ -45,15 +45,15 @@ def forward(
 
     tokenizer: BaseTokenizer = model.attrs["tokenizer"]
 
-    tokenized_texts = tokenizer(tuple(doc.text for doc in X))
+    text_batch = tokenizer(tuple(doc.text for doc in X))
 
-    for doc, text in zip(X, tokenized_texts.tokenized_texts):
+    for doc, text in zip(X, text_batch.tokenized_texts):
         doc.user_data["tokenized_text"] = text
 
     device = get_torch_default_device()
 
     (log_potentials, tag_indices), backward = model.layers[0](
-        [tokenized_texts.get_tagger_inputs(device), tokenized_texts.get_mask(device)],
+        [text_batch.get_tagger_inputs(device), text_batch.get_mask(device)],
         is_train,
     )
 

spacy_partial_tagger/tokenizer.py

@@ -4,8 +4,8 @@
 from partial_tagger.data import Span, TokenizedText
 from partial_tagger.data.batch.text import (
     BaseTokenizer,
+    TextBatch,
     Texts,
-    TokenizedTexts,
     TransformerTokenizer,
 )
 from transformers import AutoTokenizer
@@ -31,7 +31,7 @@ def __init__(
         }
         self.__tokenizer_args["return_offsets_mapping"] = True
 
-    def __call__(self, texts: Texts) -> TokenizedTexts:
+    def __call__(self, texts: Texts) -> TextBatch:
         batch_encoding = self.__tokenizer(texts, **self.__tokenizer_args)
 
         pad_token_id = self.__tokenizer.pad_token_id
@@ -62,7 +62,7 @@ def __call__(self, texts: Texts) -> TokenizedTexts:
         mask = torch.tensor(
             [[True] * length + [False] * (max_length - length) for length in lengths]
         )
-        return TokenizedTexts(tuple(tokenized_texts), batch_encoding, mask)
+        return TextBatch(tuple(tokenized_texts), batch_encoding, mask)
 
 
 def get_tokenizer(