|
--- |
|
language: |
|
- "en" |
|
thumbnail: "https://example.com/path/to/your/thumbnail.jpg" |
|
tags: |
|
- "tag1" |
|
- "tag2" |
|
license: "mit" |
|
datasets: |
|
- "dataset1" |
|
- "dataset2" |
|
metrics: |
|
- "metric1" |
|
- "metric2" |
|
--- |
|
|
|
# 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. |