Initial commit

Moved training code from 'avarias'

.gitignore

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environment.yml

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+name: huggingface_transformers_utils
+channels:
+  - conda-forge
+  - huggingface
+dependencies:
+  - transformers=4.28
+  - pip:
+      - evaluate==0.4.0

tox.ini

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+[flake8]
+max-line-length = 88
+extend-ignore = E203

trainers.py

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+import numpy as np
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorForTokenClassification,
+    Trainer,
+    TrainingArguments,
+    EvalPrediction,
+)
+from typing import Union
+from datasets import load_dataset
+from datasets.dataset_dict import (
+    DatasetDict,
+    Dataset,
+    IterableDatasetDict,
+)
+from datasets.iterable_dataset import IterableDataset
+import evaluate
+
+
+class TokenClassificationTrainer:
+    def __init__(
+        self,
+        model: str,
+        dataset: str,
+        labels_name: str = "labels",
+        evaluator: str = "seqeval",
+    ) -> None:
+        self._dataset: Union[
+            DatasetDict, Dataset, IterableDatasetDict, IterableDataset
+        ] = load_dataset(dataset)
+        self._labels: list[str] = (
+            self._dataset["train"].features[labels_name].feature.names
+        )  # type: ignore
+        self._id_to_label: dict[int, str] = {}
+        self._label_to_id: dict[str, int] = {}
+        for id, label in enumerate(self._labels):
+            self._id_to_label[id] = label
+            self._label_to_id[label] = id
+        self._model = AutoModelForSequenceClassification.from_pretrained(
+            model,
+            num_labels=len(self._labels),
+            id2label=self._id_to_label,
+            label2id=self._label_to_id,
+        )
+        self._tokenizer = AutoTokenizer.from_pretrained(model)
+        self._data_collator = DataCollatorForTokenClassification(
+            tokenizer=self._tokenizer
+        )
+        self._evaluator = evaluate.load(evaluator)
+
+    def tokenize_and_align_labels(self, examples):
+        # Straight from
+        # https://huggingface.co/docs/transformers/tasks/token_classification
+        tokenized_inputs = self._tokenizer(
+            examples["tokens"], truncation=True, is_split_into_words=True
+        )
+
+        labels = []
+        for i, label in enumerate(examples[f"ner_tags"]):
+            word_ids = tokenized_inputs.word_ids(
+                batch_index=i
+            )  # Map tokens to their respective word.
+            previous_word_idx = None
+            label_ids = []
+            for word_idx in word_ids:  # Set the special tokens to -100.
+                if word_idx is None:
+                    label_ids.append(-100)
+                elif (
+                    word_idx != previous_word_idx
+                ):  # Only label the first token of a given word.
+                    label_ids.append(label[word_idx])
+                else:
+                    label_ids.append(-100)
+                previous_word_idx = word_idx
+            labels.append(label_ids)
+
+        tokenized_inputs["labels"] = labels
+        return tokenized_inputs
+
+    def tokenize_and_align_labels_over_dataset(self):
+        return self._dataset.map(self.tokenize_and_align_labels, batched=True)
+
+    def compute_metrics(
+        self, evaluation_prediction: EvalPrediction
+    ) -> dict[str, float]:
+        predictions, expectations = evaluation_prediction
+        predictions = np.argmax(predictions, axis=2)
+
+        true_predictions = [
+            [self._labels[p] for (p, l) in zip(prediction, label) if l != -100]
+            for prediction, label in zip(predictions, expectations)
+        ]
+
+        true_labels = [
+            [self._labels[l] for (p, l) in zip(prediction, label) if l != -100]
+            for prediction, label in zip(predictions, expectations)
+        ]
+
+        results: dict[str, float] = self._evaluator.compute(
+            predictions=true_predictions, references=true_labels
+        )  # type: ignore
+        
+        return {
+            "precision": results["overall_precision"],
+            "recall": results["overall_recall"],
+            "f1": results["overall_f1"],
+            "accuracy": results["overall_accuracy"],
+        }
+
+    def train(self, output_dir: str, **arguments):
+        trainer = Trainer(
+            args=TrainingArguments(output_dir=output_dir, **arguments),
+            train_dataset=self._dataset["train"],  # type: ignore
+            eval_dataset=self._dataset["test"],  # type: ignore
+            tokenizer=self._tokenizer,
+            data_collator=self._data_collator,
+            compute_metrics=self.compute_metrics,
+        )
+
+        trainer.train()