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Update README.md

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  1. README.md +9 -11
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- ---
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- {}
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- ---
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  # 🤖 GLMMC: Generalist and Lightweight Model for Multilabel Classification
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  GLMMC is a Multilabel Classification Model capable of classifying texts into various predefined entities using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to Large Language Models (LLMs), which, despite their flexibility, are costly and too large for resource-constrained scenarios.
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  from model import BiEncoderModel
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- texts = ["A celebrity chef has opened a new restaurant specializing in vegan cuisine.",
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- "Doctors are warning about the rise in flu cases this season.",
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  "The United States has announced plans to build a wall on its border with Mexico."]
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  batch_labels = [
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-
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  ["Food", "Business", "Politics"],
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  ["Health", "Food", "Public Health"],
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  ["Immigration", "Religion", "National Security"]
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  ]
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  # Load the model
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- model = BiEncoderModel("sharki007/bi-encoder-model", max_num_labels=6)
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  # Prediction with JSON output
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  predictions = model.forward_predict(texts, batch_labels)
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  print("Predictions:", predictions)
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  #### Expected Output
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  ```
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-
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- Predictions: [{'text': 'A celebrity chef has opened a new restaurant specializing in vegan cuisine.', 'scores': {'Food': 0.71, 'Business': 0.64, 'Politics': 0.41}},
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- {'text': 'Doctors are warning about the rise in flu cases this season.', 'scores': {'Health': 0.72, 'Food': 0.49, 'Public Health': 0.7}},
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- {'text': 'The United States has announced plans to build a wall on its border with Mexico.', 'scores': {'Immigration': 0.69, 'Religion': 0.33, 'National Security': 0.72}}]
 
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  ```
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  # 🤖 GLMMC: Generalist and Lightweight Model for Multilabel Classification
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  GLMMC is a Multilabel Classification Model capable of classifying texts into various predefined entities using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to Large Language Models (LLMs), which, despite their flexibility, are costly and too large for resource-constrained scenarios.
 
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  from model import BiEncoderModel
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+ texts = ["A celebrity chef has opened a new restaurant specializing in vegan cuisine.",
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+ "Doctors are warning about the rise in flu cases this season.",
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  "The United States has announced plans to build a wall on its border with Mexico."]
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  batch_labels = [
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+
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  ["Food", "Business", "Politics"],
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  ["Health", "Food", "Public Health"],
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  ["Immigration", "Religion", "National Security"]
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  ]
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  # Load the model
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+ model = BiEncoderModel("sabdou/bi-encoder-model", max_num_labels=6)
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  # Prediction with JSON output
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  predictions = model.forward_predict(texts, batch_labels)
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  print("Predictions:", predictions)
 
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  #### Expected Output
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  ```
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+ Predictions: [
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+ {'text': 'A celebrity chef has opened a new restaurant specializing in vegan cuisine.', 'scores': {'Food': 1.0, 'Business': 1.0, 'Politics': 0.0}},
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+ {'text': 'Doctors are warning about the rise in flu cases this season.', 'scores': {'Health': 1.0, 'Food': 0.0, 'Public Health': 1.0}},
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+ {'text': 'The United States has announced plans to build a wall on its border with Mexico.', 'scores': {'Immigration': 1.0, 'Religion': 0.0, 'National Security': 1.0}
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+ ]
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  ```
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