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Update tasks/text.py
Browse filesAdd inference code with sentence transformer and XGBoost model
- tasks/text.py +21 -1
tasks/text.py
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@@ -7,6 +7,12 @@ import random
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from .utils.evaluation import TextEvaluationRequest
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from .utils.emissions import tracker, clean_emissions_data, get_space_info
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router = APIRouter()
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DESCRIPTION = "Random Baseline"
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@@ -53,12 +59,26 @@ async def evaluate_text(request: TextEvaluationRequest):
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#--------------------------------------------------------------------------------------------
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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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# Make random predictions (placeholder for actual model inference)
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true_labels = test_dataset["label"]
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#--------------------------------------------------------------------------------------------
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# YOUR MODEL INFERENCE STOPS HERE
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from .utils.evaluation import TextEvaluationRequest
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from .utils.emissions import tracker, clean_emissions_data, get_space_info
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#packages needed for inference
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from sentence_transformers import SentenceTransformer
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from xgboost import XGBClassifier
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import pickle
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router = APIRouter()
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DESCRIPTION = "Random Baseline"
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#--------------------------------------------------------------------------------------------
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# YOUR MODEL INFERENCE CODE HERE
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#Load the embedding model
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model = SentenceTransformer("dunzhang/stella_en_400M_v5",trust_remote_code=True)
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# Convert each sentence into a vector representation (embedding)
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embeddings = model.encode(test_dataset['quote'].tolist())
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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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# Make random predictions (placeholder for actual model inference)
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true_labels = test_dataset["label"]
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#load the xgboost model
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with open("models/stella_400_xgb_500.pkl",'rb') as f:
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xgbclassifier = pickle.load(f)
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#make inference
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predictions = xgbclassifier.predict(embeddings)
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#--------------------------------------------------------------------------------------------
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# YOUR MODEL INFERENCE STOPS HERE
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