56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
import pickle
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import torch
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from transformers import AutoModelForSequenceClassification
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from torch.utils.data import DataLoader
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from tqdm.auto import tqdm
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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_A.pickle','rb') as f_p:
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test_dataset = pickle.load(f_p)
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device = 'cuda'
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model = AutoModelForSequenceClassification.from_pretrained('./roberta_year_prediction/epoch_best')
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model.eval()
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model.to(device)
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lrelu = torch.nn.LeakyReLU(0.0)
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def soft_clip(t):
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t = lrelu(t)
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t = -lrelu(-t + 1 ) + 1
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return t
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with open('scalers.pickle', 'rb') as f_scaler:
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scalers = pickle.load(f_scaler)
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def predict(dataset, out_f):
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eval_dataloader = DataLoader(dataset, batch_size=10)
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outputs = []
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progress_bar = tqdm(range(len(eval_dataloader)))
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for batch in eval_dataloader:
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batch['input_ids'] = torch.stack(batch['input_ids']).permute(1,0).to(device)
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batch['attention_mask'] = torch.stack(batch['attention_mask']).permute(1,0).to(device)
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batch['labels'] = batch['year_scaled'].to(device).float()
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batch['labels'].to(device)
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batch['input_ids'].to(device)
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batch['attention_mask'].to(device)
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for c in set(batch.keys()) - {'input_ids', 'attention_mask', 'labels'}:
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del batch[c]
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outputs.extend(soft_clip(model(**batch).logits).tolist())
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progress_bar.update(1)
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outputs_transformed = scalers['year'].inverse_transform(outputs)
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with open(out_f,'w') as f_out:
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for o in outputs_transformed:
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f_out.write(str(o[0]) + '\n')
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predict(eval_dataset_full, '../dev-0/out.tsv')
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predict(test_dataset, '../test-A/out.tsv')
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