54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import pickle
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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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('train_dataset.pickle','rb') as f_p:
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# train_dataset = pickle.load(f_p)
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#
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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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#
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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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#
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#with open('test_dataset_A.pickle','rb') as f_p:
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# test_dataset_A = pickle.load(f_p)
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with open('dev-0_huggingface_format.csv','r') as f_p:
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eval_dataset_full = f_p.readlines()
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with open('test-A_huggingface_format.csv','r') as f_p:
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test_dataset = f_p.readlines()
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device = 'cuda'
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model = AutoModelForSequenceClassification.from_pretrained('./roberta_year_prediction/epoch_best')
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tokenizer = AutoTokenizer.from_pretrained('roberta-base')
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model.eval()
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model.to(device)
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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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tokenizer = AutoTokenizer.from_pretrained('roberta-base')
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def predict(dataset, out_f):
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outputs = []
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for sample in tqdm(dataset[1:]):
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y, t = sample.split('\t')
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t = t.rstrip()
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t = tokenizer(t, padding="max_length", truncation=True, max_length=512, return_tensors='pt').to('cuda')
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outputs.extend(model(**t).logits.tolist())
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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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