metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-cross-ner
results: []
bert-finetuned-cross-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1761
- Precision: 0.8267
- Recall: 0.8619
- F1: 0.8439
- Accuracy: 0.9561
Model description
More information needed
Intended uses & limitations
More information needed
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: 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2037 | 1.0 | 2607 | 0.1973 | 0.7633 | 0.8122 | 0.7870 | 0.9449 |
| 0.1264 | 2.0 | 5214 | 0.1709 | 0.8102 | 0.8484 | 0.8289 | 0.9542 |
| 0.0817 | 3.0 | 7821 | 0.1761 | 0.8267 | 0.8619 | 0.8439 | 0.9561 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3