Xlmr Lstm Crf Resume Ner2

🚀 xlmr-lstm-crf-resume-ner2

This model is a fine - tuned version of [xlm - roberta - base](https://huggingface.co/xlm - roberta - base) on the None dataset. It offers high - performance results in evaluation, providing valuable insights for related NLP tasks.

🚀 Quick Start

This fine - tuned model is ready to be applied in practical scenarios. You can start using it by referring to the official documentation of Hugging Face and loading the model with appropriate code.

✨ Features

• Fine - tuned: Based on the powerful xlm - roberta - base model, it is fine - tuned for better performance on specific tasks.
• Multiple Metrics: Achieves good results in multiple evaluation metrics such as precision, recall, F1, and accuracy.

📚 Documentation

Model Evaluation Results

This model achieves the following results on the evaluation set:

• Loss: 0.3688
• Precision: 0.7289
• Recall: 0.7578
• F1: 0.7431
• Accuracy: 0.9403

Training and Evaluation Data

More information needed

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

• learning_rate: 8e - 05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 100
• mixed_precision_training: Native AMP

Training Results

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0

📄 License

This project is licensed under the MIT license.