roberta base R T E

Introduction
The roberta-base-RTE model is a fine-tuned version of the roberta-base model for sequence classification tasks. It was developed using TextAttack and the GLUE dataset. The model is designed for text classification applications and is implemented using the Transformers library, compatible with both PyTorch and JAX.

Architecture
The model is based on the roberta-base architecture, which is a transformer model well-suited for natural language processing tasks. It employs a cross-entropy loss function for classification, allowing it to distinguish between different text sequences effectively.

Training
The model was trained for 5 epochs with a batch size of 16 and a learning rate of 2e-5. The maximum sequence length was set to 128 tokens. Training achieved an evaluation set accuracy of 0.7942, with the best results occurring after 3 epochs.

Guide: Running Locally
To run this model locally, follow these steps:

1. Clone the Repository:

   • Use git clone to clone the repository containing the model files.

2. Install Dependencies:

   • Install the necessary libraries, such as transformers and torch for PyTorch support.

3. Load the Model:

   • Use the Transformers library to load the model with from_pretrained method.

4. Run Inference:

   • Prepare your text input and use the model to predict the classification.

For enhanced performance, consider using cloud GPUs such as those provided by AWS, Google Cloud, or Azure to handle larger datasets and computations efficiently.

License
The model and its associated files are subject to the licenses provided by the Hugging Face repository and the TextAttack project. Please refer to their respective documentation for more detailed licensing information.