2-dl-container/Container-Root/job/bert/compile_model-gpu.py [20:30]:
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tokenizer = AutoTokenizer.from_pretrained(model_name)
encoded_inputs = tokenizer.encode_plus(sequence, max_length=max_length, padding='max_length', truncation=True,
                                       return_tensors='pt')
input_ids_tensor = encoded_inputs['input_ids']
batch_input_ids_tensor = torch.cat([input_ids_tensor] * batch_size)

attention_mask_tensor = encoded_inputs['attention_mask']
batch_attention_mask_tensor = torch.cat([attention_mask_tensor] * batch_size)

# Get the model and predict
orig_model = AutoModelForSequenceClassification.from_pretrained(model_name, return_dict=False)
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2-dl-container/Container-Root/job/bert/compile_model-inf.py [26:36]:
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tokenizer = AutoTokenizer.from_pretrained(model_name)
encoded_inputs = tokenizer.encode_plus(sequence, max_length=max_length, padding='max_length', truncation=True,
                                       return_tensors='pt')
input_ids_tensor = encoded_inputs['input_ids']
batch_input_ids_tensor = torch.cat([input_ids_tensor] * batch_size)
attention_mask_tensor = encoded_inputs['attention_mask']
batch_attention_mask_tensor = torch.cat([attention_mask_tensor] * batch_size)

# batch_encoded_inputs = [encoded_inputs['input_ids']] * batch_size,
# Get the model and predict
orig_model = AutoModelForSequenceClassification.from_pretrained(model_name, return_dict=False)
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