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.