Medissa commited on
Commit
3804c64
·
verified ·
1 Parent(s): a99fef1

Update tasks/text.py

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Files changed (1) hide show
  1. tasks/text.py +7 -7
tasks/text.py CHANGED
@@ -95,12 +95,12 @@ async def evaluate_text(request: TextEvaluationRequest):
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  # YOUR MODEL INFERENCE CODE HERE
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  # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
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  #--------------------------------------------------------------------------------------------
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- logger.info('Start Binary')
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  # Binary Model
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  tokenizer = AutoTokenizer.from_pretrained(BINARY_MODEL)
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  print('Loaded Tokenizer')
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  model = AutoModelForSequenceClassification.from_pretrained(BINARY_MODEL)
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- logger.info('Loaded Model')
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  model.to(device)
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  model.eval()
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@@ -117,18 +117,18 @@ async def evaluate_text(request: TextEvaluationRequest):
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  prediction = "0_not_relevant" if binary_prediction==0 else 1
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  predictions.append(prediction)
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  if i%10:
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- logger.info(f'iteration: {i}')
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  gc.collect()
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  ## 2. Taxonomy Model
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- logger.info('Start Multi')
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  tokenizer = AutoTokenizer.from_pretrained(MULTI_CLASS_MODEL)
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- logger.info('Loaded Tokenizer')
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  model = AutoModelForSequenceClassification.from_pretrained(MULTI_CLASS_MODEL)
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  model.to(device)
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  model.eval()
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- logger.info('Loaded Model')
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  for i,text in tqdm(enumerate(test_dataset["quote"])):
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  if isinstance(predictions[i], str):
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  continue
@@ -142,7 +142,7 @@ async def evaluate_text(request: TextEvaluationRequest):
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  prediction = ID2LABEL[taxonomy_prediction]
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  predictions[i] = prediction
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  if i%10:
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- logger.info(f'iteration: {i}')
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  predictions = [LABEL_MAPPING[pred] for pred in predictions]
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  #--------------------------------------------------------------------------------------------
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  # YOUR MODEL INFERENCE STOPS HERE
 
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  # YOUR MODEL INFERENCE CODE HERE
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  # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
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  #--------------------------------------------------------------------------------------------
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+ print('Start Binary')
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  # Binary Model
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  tokenizer = AutoTokenizer.from_pretrained(BINARY_MODEL)
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  print('Loaded Tokenizer')
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  model = AutoModelForSequenceClassification.from_pretrained(BINARY_MODEL)
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+ print('Loaded Model')
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  model.to(device)
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  model.eval()
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  prediction = "0_not_relevant" if binary_prediction==0 else 1
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  predictions.append(prediction)
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  if i%10:
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+ print(f'iteration: {i}')
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  gc.collect()
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  ## 2. Taxonomy Model
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+ print('Start Multi')
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  tokenizer = AutoTokenizer.from_pretrained(MULTI_CLASS_MODEL)
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+ print('Loaded Tokenizer')
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  model = AutoModelForSequenceClassification.from_pretrained(MULTI_CLASS_MODEL)
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  model.to(device)
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  model.eval()
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+ print('Loaded Model')
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  for i,text in tqdm(enumerate(test_dataset["quote"])):
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  if isinstance(predictions[i], str):
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  continue
 
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  prediction = ID2LABEL[taxonomy_prediction]
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  predictions[i] = prediction
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  if i%10:
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+ print(f'iteration: {i}')
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  predictions = [LABEL_MAPPING[pred] for pred in predictions]
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  #--------------------------------------------------------------------------------------------
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  # YOUR MODEL INFERENCE STOPS HERE