readme template

README.md

@@ -1,3 +1,58 @@
----
-license: mit
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+# 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.