Model Card for Fine-Tuned BERT for Classification

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.

Model Details

Model Description

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.

Developed by: Your Name or Organization
Funded by [optional]: [Add funding information if applicable]
Shared by [optional]: [Add sharing information if applicable]
Model type: Text Classification
Language(s) (NLP): English
License: Apache-2.0
Finetuned from model [optional]: BERT bert-base-uncased

Model Sources

Repository: [Link to your GitHub repository if available]
Paper [optional]: [Link to related paper if available]
Demo [optional]: [Link to a live demo if available]

Uses

Direct Use

This model is intended for binary text classification tasks. It can be used to classify text data into two categories.

Downstream Use

The model can be fine-tuned further for other specific binary text classification tasks by using appropriate datasets and training procedures.

Out-of-Scope Use

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.

Bias, Risks, and Limitations

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.

Recommendations

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.

How to Get Started with the Model

import torch
from transformers import BertTokenizer, BertForSequenceClassification

# Load the model and tokenizer
model = BertForSequenceClassification.from_pretrained('Darshan03/AI-Hackathon')
tokenizer = BertTokenizer.from_pretrained('Darshan03/AI-Hackathon')

# Tokenize the input text
inputs = tokenizer("Your text here", return_tensors='pt', padding=True, truncation=True, max_length=128)

# Perform inference
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()

print(f"Predicted class: {predicted_class}")