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train_args = {
'learning_rate': 1e-5,
'max_seq_length': 512,
'doc_stride': 512,
'overwrite_output_dir': True,
'reprocess_input_data': False,
'train_batch_size': 8,
'num_train_epochs': 2,
'gradient_accumulation_steps': 2,
'no_cache': True,
'use_cached_eval_features': False,
'save_model_every_epoch': False,
'output_dir': "bart-squadv2",
'eval_batch_size': 32,
'fp16_opt_level': 'O2',
}{"correct": 6961, "similar": 4359, "incorrect": 553, "eval_loss": -12.177856394381962}1from transformers import XLMRobertaTokenizer, XLMRobertaForQuestionAnswering
2import torch
3
4tokenizer = XLMRobertaTokenizer.from_pretrained('a-ware/xlmroberta-squadv2')
5model = XLMRobertaForQuestionAnswering.from_pretrained('a-ware/xlmroberta-squadv2')
6
7question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
8encoding = tokenizer(question, text, return_tensors='pt')
9input_ids = encoding['input_ids']
10attention_mask = encoding['attention_mask']
11
12start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2]
13
14all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
15answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
16answer = tokenizer.convert_tokens_to_ids(answer.split())
17answer = tokenizer.decode(answer)
18#answer => 'a nice puppet' Created with ❤️ by A-ware UGGithub icon