|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-cased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-ner-3 |
|
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-ner-3 |
|
|
|
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: 0.5646 |
|
- Precision: 0.1708 |
|
- Recall: 0.4296 |
|
- F1: 0.2444 |
|
- Accuracy: 0.8849 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 211 | 0.3086 | 0.1551 | 0.2612 | 0.1946 | 0.9151 | |
|
| No log | 2.0 | 422 | 0.3039 | 0.1730 | 0.3608 | 0.2339 | 0.9091 | |
|
| 0.3957 | 3.0 | 633 | 0.3823 | 0.1396 | 0.3608 | 0.2013 | 0.8904 | |
|
| 0.3957 | 4.0 | 844 | 0.4147 | 0.1592 | 0.3780 | 0.2240 | 0.8862 | |
|
| 0.1085 | 5.0 | 1055 | 0.4257 | 0.1785 | 0.3814 | 0.2432 | 0.8963 | |
|
| 0.1085 | 6.0 | 1266 | 0.5030 | 0.1575 | 0.4055 | 0.2269 | 0.8797 | |
|
| 0.1085 | 7.0 | 1477 | 0.5427 | 0.1509 | 0.3883 | 0.2173 | 0.8784 | |
|
| 0.0488 | 8.0 | 1688 | 0.5601 | 0.1673 | 0.4467 | 0.2434 | 0.8775 | |
|
| 0.0488 | 9.0 | 1899 | 0.5518 | 0.1707 | 0.4124 | 0.2414 | 0.8880 | |
|
| 0.0243 | 10.0 | 2110 | 0.5646 | 0.1708 | 0.4296 | 0.2444 | 0.8849 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.1 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |
|
|
