86 lines
2.6 KiB
Python
86 lines
2.6 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('roberta-ireland').cuda()
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from transformers import TrainingArguments
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training_args = TrainingArguments("roberta-ireland",
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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=2_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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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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#model = AutoModelForSequenceClassification.from_pretrained('roberta-retrained/')
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#for dataset in ('dev-0', 'test-A'):
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# with open(f'../{dataset}/in.tsv') as f_in, open(f'../{dataset}/out.tsv','w') as f_out:
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# for line_in in tqdm(f_in, total=150_000):
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# _,_, text = line_in.split('\t')
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# text = text.rstrip('\n')
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# inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt").to(device)
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# outputs = model(**inputs)
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# probs = outputs[0].softmax(1)
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# prediction = LABELS_LIST[probs.argmax(1)]
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# f_out.write(prediction + '\n')
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#
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