Bert Bilstm Crf Ner Weibo

🚀 bert_bilstm_crf-ner-weibo

This model is a fine - tuned version of google-bert/bert-base-chinese on an unknown dataset. It offers high - performance Named Entity Recognition (NER) capabilities, achieving excellent results in precision, recall, F1 score, and accuracy on the evaluation set.

🚀 Quick Start

This section provides a high - level overview of the model. For detailed usage, please refer to the official documentation of the transformers library.

✨ Features

• Fine - tuned Model: Based on the pre - trained google-bert/bert-base-chinese model, it is fine - tuned on a specific dataset to adapt to NER tasks.
• High Performance: Achieves good performance in multiple evaluation metrics such as precision, recall, F1 score, and accuracy.

📚 Documentation

Model description

This model is a fine - tuned version of google-bert/bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1945
• Precision: 0.6524
• Recall: 0.7429
• F1: 0.6947
• Accuracy: 0.9703

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.46.1
• Pytorch 1.13.1+cu117
• Datasets 3.1.0
• Tokenizers 0.20.2

🔧 Technical Details

Model Information