| --- |
| 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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|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| import numpy as np |
| from scipy.special import expit |
| |
| MODEL = "PavanDeepak/Topic_Classification" |
| tokenizer = AutoTokenizer.from_pretrained(MODEL) |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL) |
| class_mapping = model.config.id2label |
| |
| text = "I love chicken manchuria" |
| tokens = tokenizer(text, return_tensors="pt") |
| output = model(**tokens) |
| |
| scores = output.logits[0][0].detach().numpy() |
| scores = expit(scores) |
| predictions = (scores >= 0.5) * 1 |
| |
| for i in range(len(predictions)): |
| if predictions[i]: |
| print(class_mapping[i]) |
| ``` |
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| ## Output: |
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| * Food |
| * Videogames & Shows |
| * Homestyle |
| * Travel |
| * Health |
