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tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6370
- Precision: 0.5313
- Recall: 0.4530
- F1: 0.4891
- Accuracy: 0.9290
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 125 | 0.5387 | 0.2190 | 0.0552 | 0.0882 | 0.8991 |
| No log | 2.0 | 250 | 0.4241 | 0.3430 | 0.1750 | 0.2317 | 0.9117 |
| No log | 3.0 | 375 | 0.4721 | 0.3502 | 0.1786 | 0.2366 | 0.9088 |
| 0.1529 | 4.0 | 500 | 0.6204 | 0.4300 | 0.2320 | 0.3014 | 0.9134 |
| 0.1529 | 5.0 | 625 | 0.6479 | 0.4470 | 0.2486 | 0.3195 | 0.9104 |
| 0.1529 | 6.0 | 750 | 0.4640 | 0.4532 | 0.4015 | 0.4258 | 0.9220 |
| 0.1529 | 7.0 | 875 | 0.5170 | 0.4288 | 0.4217 | 0.4253 | 0.9224 |
| 0.0229 | 8.0 | 1000 | 0.5846 | 0.5524 | 0.4273 | 0.4818 | 0.9233 |
| 0.0229 | 9.0 | 1125 | 0.5569 | 0.4644 | 0.4328 | 0.4480 | 0.9234 |
| 0.0229 | 10.0 | 1250 | 0.5818 | 0.5502 | 0.4438 | 0.4913 | 0.9258 |
| 0.0229 | 11.0 | 1375 | 0.6183 | 0.5607 | 0.4254 | 0.4838 | 0.9231 |
| 0.0048 | 12.0 | 1500 | 0.6148 | 0.5385 | 0.4254 | 0.4753 | 0.9250 |
| 0.0048 | 13.0 | 1625 | 0.6271 | 0.4896 | 0.4328 | 0.4594 | 0.9255 |
| 0.0048 | 14.0 | 1750 | 0.6475 | 0.5668 | 0.4217 | 0.4836 | 0.9267 |
| 0.0048 | 15.0 | 1875 | 0.6428 | 0.5704 | 0.4328 | 0.4921 | 0.9282 |
| 0.0016 | 16.0 | 2000 | 0.6577 | 0.5487 | 0.4254 | 0.4793 | 0.9270 |
| 0.0016 | 17.0 | 2125 | 0.6688 | 0.5556 | 0.4144 | 0.4747 | 0.9262 |
| 0.0016 | 18.0 | 2250 | 0.6481 | 0.5434 | 0.4383 | 0.4852 | 0.9282 |
| 0.0016 | 19.0 | 2375 | 0.6432 | 0.5428 | 0.4438 | 0.4883 | 0.9289 |
| 0.0007 | 20.0 | 2500 | 0.6370 | 0.5313 | 0.4530 | 0.4891 | 0.9290 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.8.0
- Datasets 2.6.1
- Tokenizers 0.13.1
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