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import numpy as np |
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import transformers |
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from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer, AutoTokenizer |
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from transformers import DataCollatorForTokenClassification |
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from datasets import load_dataset, load_metric |
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import os |
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from pathlib import Path |
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import datasets |
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from datasets import DownloadConfig |
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import os |
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from pathlib import Path |
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from datasets import load_dataset, ClassLabel, DownloadConfig |
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from transformers import AutoTokenizer |
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from hf_tokenize import HFTokenizer |
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from hf_dataset import JPNDataset |
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metric = load_metric("seqeval") |
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logger = datasets.logging.get_logger(__name__) |
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def compute_metrics(p, label_list): |
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predictions, labels = p |
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predictions = np.argmax(predictions, axis=2) |
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true_predictions = [ |
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[label_list[p] for (p, l) in zip(prediction, label) if l != -100] |
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for prediction, label in zip(predictions, labels) |
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] |
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true_labels = [ |
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[label_list[l] for (p, l) in zip(prediction, label) if l != -100] |
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for prediction, label in zip(predictions, labels) |
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] |
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results = metric.compute(predictions=true_predictions, references=true_labels) |
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return { |
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"precision": results["overall_precision"], |
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"recall": results["overall_recall"], |
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"f1": results["overall_f1"], |
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"accuracy": results["overall_accuracy"], |
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} |
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if __name__ == "__main__": |
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model_n_version = "jpn202401" |
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max_epochs = 150 |
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learning_rate = 2e-5 |
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batch_size = 12 |
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model_root_dir = "." |
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hf_pretrained_model_checkpoint = "distilbert-base-uncased" |
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hf_pretrained_tokenizer_checkpoint = "distilbert-base-uncased" |
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hf_dataset = JPNDataset() |
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hf_preprocessor = HFTokenizer.init_vf(hf_pretrained_tokenizer_checkpoint=hf_pretrained_tokenizer_checkpoint) |
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hf_model = AutoModelForTokenClassification.from_pretrained(hf_pretrained_model_checkpoint, |
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num_labels=len(hf_dataset.labels)) |
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hf_model.config.id2label = hf_dataset.id2label |
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hf_model.config.label2id = hf_dataset.label2id |
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tokenized_datasets = hf_dataset.dataset.map(hf_preprocessor.tokenize_and_align_labels, batched=True) |
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args = TrainingArguments( |
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f"jpn202401", |
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evaluation_strategy="epoch", |
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learning_rate=learning_rate, |
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per_device_train_batch_size=batch_size, |
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per_device_eval_batch_size=batch_size, |
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num_train_epochs=max_epochs, |
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weight_decay=0.01, |
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) |
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data_collator = DataCollatorForTokenClassification(hf_preprocessor.tokenizer) |
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trainer = Trainer( |
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hf_model, |
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args, |
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train_dataset=tokenized_datasets["train"], |
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eval_dataset=tokenized_datasets["test"], |
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data_collator=data_collator, |
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tokenizer=hf_preprocessor.tokenizer, |
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compute_metrics=lambda p: compute_metrics(p=p, label_list=hf_dataset.labels) |
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) |
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trainer.train() |
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trainer.evaluate() |
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predictions, labels, _ = trainer.predict(tokenized_datasets["test"]) |
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predictions = np.argmax(predictions, axis=2) |
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true_predictions = [ |
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[hf_dataset.labels[p] for (p, l) in zip(prediction, label) if l != -100] |
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for prediction, label in zip(predictions, labels) |
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] |
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true_labels = [ |
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[hf_dataset.labels[l] for (p, l) in zip(prediction, label) if l != -100] |
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for prediction, label in zip(predictions, labels) |
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] |
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results = metric.compute(predictions=true_predictions, references=true_labels) |
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print(results) |
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out_dir = os.path.expanduser(model_root_dir) + "/" + model_n_version |
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trainer.save_model(out_dir) |
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