| | --- |
| | license: mit |
| | --- |
| | ## BERT-based Text Classification Model |
| | This model is a fine-tuned version of the bert-base-uncased model, specifically adapted for text classification across a diverse set of categories. The model has been trained on a dataset collected from multiple sources, including the News Category Dataset on Kaggle and various other websites. |
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| | The model classifies text into one of the following 12 categories: |
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| | * Food |
| | * Videogames & Shows |
| | * Kids and fun |
| | * Homestyle |
| | * Travel |
| | * Health |
| | * Charity |
| | * Electronics & Technology |
| | * Sports |
| | * Cultural & Music |
| | * Education |
| | * Convenience |
| | The model has demonstrated robust performance with an accuracy of 0.721459, F1 score of 0.659451, precision of 0.707620, and recall of 0.635155. |
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| | ## Model Architecture |
| | The model leverages the BertForSequenceClassification architecture, It has been fine-tuned on the aforementioned dataset, with the following key configuration parameters: |
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| | * Hidden size: 768 |
| | * Number of attention heads: 12 |
| | * Number of hidden layers: 12 |
| | * Max position embeddings: 512 |
| | * Type vocab size: 2 |
| | * Vocab size: 30522 |
| | * The model uses the GELU activation function in its hidden layers and applies dropout with a probability of 0.1 to the attention probabilities to prevent overfitting. |
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| | ## Example |
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| | ```from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification``` |
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| | ```from transformers import AutoTokenizer``` |
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| | ```import numpy as np``` |
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| | ```from scipy.special import expit``` |
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| | ```MODEL = f"PavanDeepak/Topic_Classification"``` |
| | ```tokenizer = AutoTokenizer.from_pretrained(MODEL)``` |
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| | ```model = AutoModelForSequenceClassification.from_pretrained(MODEL)``` |
| | ```class_mapping = model.config.id2label``` |
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| | ```text = "I love chicken manchuria"``` |
| | ```tokens = tokenizer(text, return_tensors='pt')``` |
| | ```output = model(**tokens)``` |
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| | ```scores = output[0][0].detach().numpy()``` |
| | ```scores = expit(scores)``` |
| | ```predictions = (scores >= 0.5) * 1``` |
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| | ```for i in range(len(predictions)):``` |
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| | ```if predictions[i]:``` |
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| | ```print(class_mapping[i])``` |
| | |
| | ## Output: |
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| | * Food |
| | * Videogames & Shows |
| | * Homestyle |
| | * Travel |
| | * Health |
