49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
|
import pickle
|
||
|
|
import torch
|
||
|
|
from transformers import AutoModelForSequenceClassification
|
||
|
|
from torch.utils.data import DataLoader
|
||
|
|
from tqdm.auto import tqdm
|
||
|
|
|
||
|
|
with open('eval_dataset_full.pickle','rb') as f_p:
|
||
|
|
eval_dataset_full = pickle.load(f_p)
|
||
|
|
|
||
|
|
with open('test_dataset_A.pickle','rb') as f_p:
|
||
|
|
test_dataset = pickle.load(f_p)
|
||
|
|
|
||
|
|
device = 'cuda'
|
||
|
|
model = AutoModelForSequenceClassification.from_pretrained('./roberta_year_prediction/epoch_best')
|
||
|
|
model.eval()
|
||
|
|
model.to(device)
|
||
|
|
|
||
|
|
with open('scalers.pickle', 'rb') as f_scaler:
|
||
|
|
scalers = pickle.load(f_scaler)
|
||
|
|
|
||
|
|
def predict(dataset, out_f):
|
||
|
|
eval_dataloader = DataLoader(dataset, batch_size=1)
|
||
|
|
outputs = []
|
||
|
|
|
||
|
|
progress_bar = tqdm(range(len(eval_dataloader)))
|
||
|
|
|
||
|
|
for batch in eval_dataloader:
|
||
|
|
batch['input_ids'] = torch.stack(batch['input_ids']).permute(1,0).to(device)
|
||
|
|
batch['attention_mask'] = torch.stack(batch['attention_mask']).permute(1,0).to(device)
|
||
|
|
batch['labels'] = batch['year_middle_float_scaled'].to(device).float()
|
||
|
|
|
||
|
|
batch['labels'].to(device)
|
||
|
|
batch['input_ids'].to(device)
|
||
|
|
batch['attention_mask'].to(device)
|
||
|
|
|
||
|
|
for c in set(batch.keys()) - {'input_ids', 'attention_mask', 'labels'}:
|
||
|
|
del batch[c]
|
||
|
|
outputs.extend(model(**batch).logits.tolist())
|
||
|
|
progress_bar.update(1)
|
||
|
|
outputs_transformed = scalers['year_middle_float'].inverse_transform(outputs)
|
||
|
|
|
||
|
|
with open(out_f,'w') as f_out:
|
||
|
|
|
||
|
|
for o in outputs_transformed:
|
||
|
|
f_out.write(str(o[0]) + '\n')
|
||
|
|
|
||
|
|
predict(eval_dataset_full, '../dev-0/out.tsv')
|
||
|
|
predict(test_dataset, '../test-A/out.tsv')
|