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--- |
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license: mit |
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--- |
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## BERT-based Text Classification Model |
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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 |
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* Videogames & Shows |
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* Kids and fun |
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* Homestyle |
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* Travel |
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* Health |
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* Charity |
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* Electronics & Technology |
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* Sports |
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* Cultural & Music |
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* Education |
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* Convenience |
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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 |
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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 |
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* Number of attention heads: 12 |
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* Number of hidden layers: 12 |
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* Max position embeddings: 512 |
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* Type vocab size: 2 |
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* Vocab size: 30522 |
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* 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"``` |
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```tokenizer = AutoTokenizer.from_pretrained(MODEL)``` |
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```model = AutoModelForSequenceClassification.from_pretrained(MODEL)``` |
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```class_mapping = model.config.id2label``` |
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```text = "I love chicken manchuria"``` |
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```tokens = tokenizer(text, return_tensors='pt')``` |
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```output = model(**tokens)``` |
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```scores = output[0][0].detach().numpy()``` |
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```scores = expit(scores)``` |
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```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])``` |
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## Output: |
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* Food |
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* Videogames & Shows |
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* Homestyle |
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* Travel |
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* Health |
