Nonnormalizable commited on
Commit
d11f2f9
·
1 Parent(s): cc921f8
Files changed (1) hide show
  1. tasks/text.py +13 -8
tasks/text.py CHANGED
@@ -11,7 +11,17 @@ router = APIRouter()
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  DESCRIPTION = "Most common class baseline"
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  ROUTE = "/text"
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-
 
 
 
 
 
 
 
 
 
 
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  @router.post(ROUTE, tags=["Text Task"],
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  description=DESCRIPTION)
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  async def evaluate_text(request: TextEvaluationRequest):
@@ -55,15 +65,10 @@ 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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-
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- # Make random predictions (placeholder for actual model inference)
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- #true_labels = test_dataset["label"]
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- #predictions = [random.randint(0, 7) for _ in range(len(true_labels))]
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- # My favorate baseline is the most common class.
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  true_labels = test_dataset["label"]
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- predictions = [0] * len(true_labels)
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-
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  #--------------------------------------------------------------------------------------------
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  # YOUR MODEL INFERENCE STOPS HERE
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  #--------------------------------------------------------------------------------------------
 
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  DESCRIPTION = "Most common class baseline"
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  ROUTE = "/text"
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+
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+ def baseline_model(dataset_length):
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+ # Make random predictions (placeholder for actual model inference)
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+ #predictions = [random.randint(0, 7) for _ in range(dataset_length)]
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+
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+ # My favorate baseline is the most common class.
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+ predictions = [0] * dataset_length
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+
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+ return predictions
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+
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+
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  @router.post(ROUTE, tags=["Text Task"],
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  description=DESCRIPTION)
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  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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  true_labels = test_dataset["label"]
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+ predictions = baseline_model(len(true_labels))
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+
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  #--------------------------------------------------------------------------------------------
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  # YOUR MODEL INFERENCE STOPS HERE
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  #--------------------------------------------------------------------------------------------