metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-ner-chunks
results: []
bert-ner-chunks
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2207
- Precision: 0.8917
- Recall: 0.9214
- F1: 0.9063
- Accuracy: 0.9730
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.077 | 1.0 | 1756 | 0.0741 | 0.9068 | 0.9334 | 0.9199 | 0.9795 |
| 0.0443 | 2.0 | 3512 | 0.0649 | 0.9172 | 0.9428 | 0.9298 | 0.9841 |
| 0.0265 | 3.0 | 5268 | 0.0578 | 0.9250 | 0.9488 | 0.9368 | 0.9863 |
| 0.0109 | 4.0 | 7024 | 0.0651 | 0.9347 | 0.9492 | 0.9419 | 0.9866 |
| 0.005 | 5.0 | 8780 | 0.0692 | 0.9354 | 0.9502 | 0.9427 | 0.9869 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0