def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = AlbertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
class AlbertQA:
""" Class to use Albert to answer questions.
TODO: Update model and checkpoints to work with last versions of transformers """
def __init__(self, path: str, device: str = 'cpu'):
""" Init the QA Albert """
if not os.path.exists(path):
raise NotADirectoryError(
f"{os.path.abspath(path)} must be a directory containing the model files: config, tokenizer, weights.")
files = os.listdir(path)
if CONFIG_JSON_FILE not in files:
raise FileNotFoundError(f"{CONFIG_JSON_FILE} must be in {path}.")
if WEIGHTS_FILE not in files:
raise FileNotFoundError(f"{WEIGHTS_FILE} must be in {path}.")
with open(os.path.join(path, CONFIG_JSON_FILE), "r") as f:
config = json.load(f)
self.tokenizer = AutoTokenizer.from_pretrained(path)
weights = torch.load(os.path.join(path, WEIGHTS_FILE),
map_location=lambda storage, loc: storage)
# Load pretrained model/tokenizer
config = AlbertConfig.from_dict(config)
self.model = AlbertForQuestionAnswering(config)
self.model.load_state_dict(weights)
self.model = self.model.eval()
self.args = albert_args_squad
if device == "cuda":
logger.debug("Setting model with CUDA")
self.args['device'] = 'cuda'
self.model.to('cuda')
def answer(self, question: str, context: str, **kwargs: dict) -> str:
""" Look the answer to question in context
Keyword Arguments:
:param question: Question to answer
:param context: Context to look for the answer into
:return: Answer to question
"""
for key in kwargs:
if key in self.args:
self.args[key] = kwargs[key]
inputs = self.tokenizer.encode_plus(question, context, **self.args)
for key in inputs.keys():
inputs[key] = inputs[key].to(self.args['device'])
input_ids = inputs["input_ids"].tolist()[0]
answer_start_scores, answer_end_scores = self.model(**inputs)
answer_start = torch.argmax(answer_start_scores) # Get the most likely beginning of answer
answer_end = torch.argmax(answer_end_scores) + 1 # Get the most likely end of answer
answer = self.tokenizer.convert_tokens_to_string(
self.tokenizer.convert_ids_to_tokens(
input_ids[answer_start:answer_end]
)
)
answer = answer.replace("[CLS]", "").replace("[SEP]", " ").replace("<s>", "").replace("</s>", "")
return answer
def create_and_check_albert_for_question_answering(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
model = AlbertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertListEqual(list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)