|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-finetuned-ner-10epochs |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# bert-finetuned-ner-10epochs |
|
|
|
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0895 |
|
- Precision: 0.9167 |
|
- Recall: 0.9546 |
|
- F1: 0.9352 |
|
- Accuracy: 0.9888 |
|
|
|
## 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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0061 | 1.0 | 2261 | 0.0961 | 0.8813 | 0.9436 | 0.9114 | 0.9869 | |
|
| 0.0241 | 2.0 | 4522 | 0.0566 | 0.9001 | 0.9502 | 0.9245 | 0.9878 | |
|
| 0.02 | 3.0 | 6783 | 0.0560 | 0.9010 | 0.9528 | 0.9261 | 0.9879 | |
|
| 0.0169 | 4.0 | 9044 | 0.0519 | 0.9045 | 0.9539 | 0.9285 | 0.9884 | |
|
| 0.0129 | 5.0 | 11305 | 0.0621 | 0.9073 | 0.9568 | 0.9314 | 0.9886 | |
|
| 0.009 | 6.0 | 13566 | 0.0623 | 0.9123 | 0.9451 | 0.9284 | 0.9883 | |
|
| 0.0078 | 7.0 | 15827 | 0.0727 | 0.9145 | 0.9473 | 0.9306 | 0.9886 | |
|
| 0.0056 | 8.0 | 18088 | 0.0806 | 0.9134 | 0.9535 | 0.9330 | 0.9882 | |
|
| 0.0034 | 9.0 | 20349 | 0.0856 | 0.9103 | 0.9546 | 0.9319 | 0.9886 | |
|
| 0.003 | 10.0 | 22610 | 0.0895 | 0.9167 | 0.9546 | 0.9352 | 0.9888 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.30.1 |
|
- Pytorch 2.0.1+cu117 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
|
|
