Distilbert Base Uncased Finetuned Ner

🚀 distilbert-base-uncased-finetuned-ner

This model is a fine - tuned version of distilbert-base-uncased on the conll2003 dataset, which can be used for token classification tasks and has achieved excellent performance on the evaluation set.

📚 Documentation

This model is a fine - tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0611
• Precision: 0.9210
• Recall: 0.9357
• F1: 0.9283
• Accuracy: 0.9832

📊 Model Information

📈 Model Index

• Name: distilbert-base-uncased-finetuned-ner
• Results:
  • Task:
    • Name: Token Classification
    • Type: token - classification
  • Dataset:
    • Name: conll2003
    • Type: conll2003
    • Args: conll2003
  • Metrics:
    • Name: Precision, Type: precision, Value: 0.9210439378923027
    • Name: Recall, Type: recall, Value: 0.9356751314464705
    • Name: F1, Type: f1, Value: 0.9283018867924528
    • Name: Accuracy, Type: accuracy, Value: 0.983176322938345

🔧 Technical Details

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 16
• eval_batch_size: 16
• 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 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0

📄 License

This model is licensed under the Apache 2.0 license.