Roberta Base Culinary

🚀 roberta-base-culinary

This model is a fine - tuned version of bert-base-uncased on the None dataset. It achieves a loss of 1.1032 on the evaluation set, offering potential for related natural language processing tasks.

🚀 Quick Start

This section will guide you through the basic information of the roberta-base-culinary model.

✨ Features

• Fine - tuned Model: Based on the bert-base-uncased model, it is fine - tuned on a specific dataset to better adapt to relevant tasks.
• Evaluated Performance: Achieves a loss of 1.1032 on the evaluation set, which can serve as a reference for model performance.

📦 Installation

The original README does not provide installation steps, so this section is skipped.

💻 Usage Examples

The original README does not provide code examples, so this section is skipped.

📚 Documentation

Model description

This model is a fine - tuned version of bert-base-uncased on the None dataset.

Intended uses & limitations

More information needed.

Training and evaluation data

More information needed.

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 16

Training results

Framework versions

• Transformers 4.22.1
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1

📄 License

This model is released under the Apache - 2.0 license.