paper_feedback_intent

Introduction

The PAPER_FEEDBACK_INTENT model is a fine-tuned version of the roberta-base model, designed for text classification tasks. It achieves high performance with the following metrics on the evaluation set:

• Loss: 0.3621
• Accuracy: 0.9302
• Precision: 0.9307
• Recall: 0.9302
• F1 Score: 0.9297

Architecture

This model is based on the roberta-base architecture, a transformer model developed by Hugging Face. It has been fine-tuned for specific tasks using a dataset that has not been disclosed.

Training

The model was trained using the following hyperparameters:

• Learning Rate: 2e-05
• Train Batch Size: 16
• Evaluation Batch Size: 16
• Seed: 42
• Optimizer: Adam with betas (0.9, 0.999) and epsilon 1e-08
• Learning Rate Scheduler Type: Linear
• Number of Epochs: 10

Training was conducted with performance evaluation at each epoch, showing improvements in loss and accuracy over time.

Guide: Running Locally

To run the model locally, you can follow these general steps:

1. Install Dependencies:

   • Ensure you have Python and pip installed.
   • Install PyTorch and the Transformers library:

     pip install torch
     pip install transformers
     

2. Download and Load the Model:

   • Use the Hugging Face Model Hub to load the model:

     from transformers import AutoModelForSequenceClassification, AutoTokenizer
     
     model = AutoModelForSequenceClassification.from_pretrained("mp6kv/paper_feedback_intent")
     tokenizer = AutoTokenizer.from_pretrained("mp6kv/paper_feedback_intent")
     

3. Inference:

   • Prepare your input data and use the model for predictions.
   • Refer to the Transformers documentation for additional details on performing inference.

4. Hardware Recommendations:

   • For optimal performance, especially with large datasets, consider using cloud-based GPUs like those offered by AWS, GCP, or Azure.

License

The model is available under the MIT License, allowing for both personal and commercial use with minimal restrictions.