Bert Finetuned Radarr

🚀 bert-finetuned-radarr

This model is a fine - tuned version of distilbert-base-uncased on the movie_releases dataset. It can effectively perform token classification tasks on movie - related data, achieving high precision, recall, F1 score, and accuracy.

🚀 Quick Start

This section is not provided in the original document.

✨ Features

• This model is fine - tuned on the movie_releases dataset, which is suitable for token classification tasks related to movie releases.
• It has achieved excellent performance in evaluation metrics such as precision, recall, F1 score, and accuracy.

📦 Installation

This section is not provided in the original document.

📚 Documentation

Model Information

Evaluation Results

This model achieves the following results on the evaluation set:

• Loss: 0.0731
• Precision: 0.9555
• Recall: 0.9639
• F1: 0.9597
• Accuracy: 0.9818

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: 3

Training Results

Framework Versions

• Transformers 4.20.1
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1

🔧 Technical Details

This section is not provided in the original document.

📄 License

This model is licensed under the Apache - 2.0 license.