| from preprocess import Model, SquadDataset |
| from transformers import DistilBertForQuestionAnswering |
| from torch.utils.data import DataLoader |
| from transformers import AdamW |
| import torch |
| import subprocess |
|
|
| data = Model() |
| train_contexts, train_questions, train_answers = data.ArrangeData("livecheckcontainer") |
| val_contexts, val_questions, val_answers = data.ArrangeData("livecheckcontainer") |
| print(train_answers) |
|
|
| train_answers, train_contexts = data.add_end_idx(train_answers, train_contexts) |
| val_answers, val_contexts = data.add_end_idx(val_answers, val_contexts) |
|
|
| train_encodings, val_encodings = data.Tokenizer(train_contexts, train_questions, val_contexts, val_questions) |
|
|
| train_encodings = data.add_token_positions(train_encodings, train_answers) |
| val_encodings = data.add_token_positions(val_encodings, val_answers) |
|
|
| train_dataset = SquadDataset(train_encodings) |
| val_dataset = SquadDataset(val_encodings) |
|
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|
| model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased") |
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|
| device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') |
|
|
| model.to(device) |
| model.train() |
|
|
| train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) |
|
|
| optim = AdamW(model.parameters(), lr=5e-5) |
|
|
| for epoch in range(2): |
| print(epoch) |
| for batch in train_loader: |
| optim.zero_grad() |
| input_ids = batch['input_ids'].to(device) |
| attention_mask = batch['attention_mask'].to(device) |
| start_positions = batch['start_positions'].to(device) |
| end_positions = batch['end_positions'].to(device) |
| outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions) |
| loss = outputs[0] |
| loss.backward() |
| optim.step() |
| print("Done") |
| model.eval() |
| model.save_pretrained("./") |
| data.tokenizer.save_pretrained("./") |
|
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|
|
| subprocess.call(["git", "add","--all"]) |
| subprocess.call(["git", "status"]) |
| subprocess.call(["git", "commit", "-m", "First version of the your-model-name model and tokenizer."]) |
| subprocess.call(["git", "push"]) |
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