my-first-hf-model

my-first-hf-model

This repository contains a fine-tuned BERT model for sequence classification tasks. The model is built using the Hugging Face Transformers library and is designed for [describe your specific use case].

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/my-first-hf-model")
model = AutoModelForSequenceClassification.from_pretrained("your-username/my-first-hf-model")

# Prepare your input
text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)

# Get predictions
outputs = model(**inputs)
predictions = outputs.logits

Model Details

This model is based on the BERT architecture and has been fine-tuned for sequence classification tasks. For detailed information about the model's architecture, training process, and performance metrics, please refer to the model card.

Requirements

• Python 3.6+
• PyTorch 1.7+
• Transformers 4.0+

To install the required packages:

pip install torch transformers

Usage Examples

# Example 1: Basic Classification
from transformers import pipeline

classifier = pipeline("text-classification", model="your-username/my-first-hf-model")
result = classifier("Your text here")
print(result)

# Example 2: Batch Processing
texts = ["First text", "Second text", "Third text"]
results = classifier(texts, batch_size=2)
print(results)

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Citation

If you use this model in your research, please cite:

@misc{my-first-hf-model,
  author = {uttambhagaur},
  title = {my-first-hf-model: A Fine-tuned BERT Model for Sequence Classification},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://huggingface.co/uttambhagaur/my-first-hf-model}}
}