77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
import pickle
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from config import LABELS_LIST, MODEL
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with open('train_dataset.pickle','rb') as f_p:
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train_dataset = pickle.load(f_p)
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with open('eval_dataset_small.pickle','rb') as f_p:
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eval_dataset_small = pickle.load(f_p)
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with open('eval_dataset_full.pickle','rb') as f_p:
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eval_dataset_full = pickle.load(f_p)
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with open('test_dataset.pickle','rb') as f_p:
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test_dataset = pickle.load(f_p)
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from transformers import AutoModelForSequenceClassification
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model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=7)
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from transformers import TrainingArguments
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training_args = TrainingArguments("test_trainer",
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per_device_train_batch_size=4,
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per_device_eval_batch_size=4,
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evaluation_strategy='steps',
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#eval_steps=2_000,
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#save_steps=2_000,
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eval_steps=20_000,
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save_steps=20_000,
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num_train_epochs=1,
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gradient_accumulation_steps=2,
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learning_rate = 1e-6,
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#warmup_steps=4_000,
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warmup_steps=4,
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load_best_model_at_end=True,
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)
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import numpy as np
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from datasets import load_metric
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metric = load_metric("accuracy")
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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predictions = np.argmax(logits, axis=-1)
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return metric.compute(predictions=predictions, references=labels)
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from transformers import Trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset_small,
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compute_metrics=compute_metrics,
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)
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#trainer.train(resume_from_checkpoint=True)
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trainer.train()
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trainer.save_model("./roberta-retrained")
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trainer.evaluate()
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eval_predictions = trainer.predict(eval_dataset_full).predictions.argmax(1)
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with open('../dev-0/out.tsv', 'w') as f_out:
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for pred in eval_predictions:
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f_out.write(LABELS_LIST[pred] + '\n')
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test_predictions = trainer.predict(test_dataset).predictions.argmax(1)
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with open('../test-A/out.tsv', 'w') as f_out:
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for pred in test_predictions:
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f_out.write(LABELS_LIST[pred] + '\n')
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