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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Fine-Tuned BERT for Classification |
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This model is a fine-tuned version of BERT for binary text classification tasks. It was trained on a specific dataset for classification purposes and is intended for use in text classification applications. |
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## Model Details |
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### Model Description |
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This BERT model has been fine-tuned for binary text classification. It is based on the `bert-base-uncased` model and has been trained to classify text into two categories: Class 0 and Class 1. |
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- **Developed by:** Your Name or Organization |
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- **Funded by [optional]:** [Add funding information if applicable] |
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- **Shared by [optional]:** [Add sharing information if applicable] |
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- **Model type:** Text Classification |
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- **Language(s) (NLP):** English |
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- **License:** Apache-2.0 |
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- **Finetuned from model [optional]:** BERT `bert-base-uncased` |
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### Model Sources |
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- **Repository:** [Link to your GitHub repository if available] |
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- **Paper [optional]:** [Link to related paper if available] |
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- **Demo [optional]:** [Link to a live demo if available] |
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## Uses |
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### Direct Use |
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This model is intended for binary text classification tasks. It can be used to classify text data into two categories. |
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### Downstream Use |
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The model can be fine-tuned further for other specific binary text classification tasks by using appropriate datasets and training procedures. |
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### Out-of-Scope Use |
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The model is not intended for use in tasks other than binary text classification. Misuse includes any application that requires multi-class classification or tasks beyond the scope of text classification. |
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## Bias, Risks, and Limitations |
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This model inherits biases present in the pre-trained BERT model and the fine-tuning dataset. Users should be cautious of potential biases related to language, context, and dataset-specific characteristics. |
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### Recommendations |
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Users should evaluate the model on their specific tasks and datasets to ensure it performs as expected. It is recommended to perform bias and fairness checks before deploying the model in production. |
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## How to Get Started with the Model |
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```python |
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import torch |
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from transformers import BertTokenizer, BertForSequenceClassification |
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# Load the model and tokenizer |
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model = BertForSequenceClassification.from_pretrained('Darshan03/AI-Hackathon') |
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tokenizer = BertTokenizer.from_pretrained('Darshan03/AI-Hackathon') |
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# Tokenize the input text |
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inputs = tokenizer("Your text here", return_tensors='pt', padding=True, truncation=True, max_length=128) |
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# Perform inference |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class = torch.argmax(logits, dim=1).item() |
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print(f"Predicted class: {predicted_class}") |
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