---
language:
  - "ISO 639-1 code for language 1" # For example, "en" for English
  - "ISO 639-1 code for language 2" # For example, "fr" for French
thumbnail: "https://example.com/path/to/your/thumbnail.jpg" # URL to a thumbnail used in social sharing
tags:
- "tag1" # For example, "sentiment-analysis"
- "tag2" # For example, "machine-translation"
license: "mit"
datasets:
- "dataset1" # For example, "imdb"
- "dataset2" # For example, "wmt16"
metrics:
- "metric1" # For example, "accuracy"
- "metric2" # For example, "f1"
---

# Your Model Name

## Introduction

This is a brief introduction about your transformer-based model. Here, you can mention the type of the model, the task it was trained for, its performance, and other key features or highlights.

## Training

Here, give detailed information about how the model was trained:

- Dataset(s) used for training
- Preprocessing techniques used
- Training configuration such as the batch size, learning rate, optimizer, number of epochs, etc.
- Any specific challenges or notable aspects of the training process

## Usage

Provide examples of how to use the model for inference. You can provide both a simple usage case and a more complex one if necessary. Make sure to explain what the inputs and outputs are.

Here's a basic example:

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("your-model-name")
model = AutoModel.from_pretrained("your-model-name")

inputs = tokenizer("Your example sentence", return_tensors="pt")
outputs = model(**inputs)

# Explain what the outputs are

## Evaluation

Discuss how the model was evaluated, which metrics were used, and what results it achieved.

## Limitations and Bias

Every model has its limitations and may have certain biases due to the data it was trained on. Explain those here.

## About Us

A small introduction about you or your team. 

## Acknowledgments

Thank people, organizations or mention the resources that helped you in this work.

## License

This model is distributed under the MIT license.

## Contact

Provide a contact method (e.g., email or GitHub issues) for people to reach out with questions, comments, or concerns.

## References

List any relevant references for your model here.