Update README.md

README.md

@@ -14,61 +14,8 @@ metrics:
 - "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.
+# TransformerBeta
 
 ## 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.