XLM RoBERTa Xtreme En

🚀 XLM-RoBERTa-xtreme-en

This model is a fine - tuned version of [xlm - roberta - base](https://huggingface.co/xlm - roberta - base) on the xtreme_en dataset. It can effectively perform token classification tasks and achieve high accuracy and F1 scores on the evaluation set.

🚀 Quick Start

This model can be used directly for token classification tasks. You can use the following code examples to quickly start using the model:

# Here should be the code example for using the model, but it's not provided in the original document.

✨ Features

• Fine - tuned on xtreme_en: This model is fine - tuned on the xtreme_en dataset, which makes it perform well in relevant tasks.
• High performance: It achieves an accuracy of 0.9109 and an F1 score of 0.7544 on the evaluation set.

📦 Installation

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

💻 Usage Examples

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

📚 Documentation

Model description

This model is a fine - tuned version of [xlm - roberta - base](https://huggingface.co/xlm - roberta - base) on the xtreme_en dataset. More detailed information is yet to be supplemented.

Intended uses & limitations

More information about the intended uses and limitations of this model is needed.

Training and evaluation data

More information about the training and evaluation data is needed.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 32
• eval_batch_size: 32
• 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

📄 License

This model is released under the MIT license.

📊 Model Information