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---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-srb-ner-setimes
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9632618605436434
---
<!-- 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-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1682
- Precision: 0.8153
- Recall: 0.8353
- F1: 0.8251
- Accuracy: 0.9633
## 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: 16
- 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 | 207 | 0.2003 | 0.6991 | 0.7440 | 0.7209 | 0.9415 |
| No log | 2.0 | 414 | 0.1597 | 0.7202 | 0.7770 | 0.7475 | 0.9509 |
| 0.2342 | 3.0 | 621 | 0.1505 | 0.7619 | 0.8012 | 0.7811 | 0.9558 |
| 0.2342 | 4.0 | 828 | 0.1710 | 0.7822 | 0.8020 | 0.7920 | 0.9564 |
| 0.082 | 5.0 | 1035 | 0.1434 | 0.7969 | 0.8277 | 0.8120 | 0.9614 |
| 0.082 | 6.0 | 1242 | 0.1526 | 0.8001 | 0.8350 | 0.8171 | 0.9625 |
| 0.082 | 7.0 | 1449 | 0.1590 | 0.8061 | 0.8384 | 0.8219 | 0.9633 |
| 0.0353 | 8.0 | 1656 | 0.1616 | 0.8143 | 0.8296 | 0.8219 | 0.9634 |
| 0.0353 | 9.0 | 1863 | 0.1684 | 0.8167 | 0.8367 | 0.8265 | 0.9631 |
| 0.017 | 10.0 | 2070 | 0.1682 | 0.8153 | 0.8353 | 0.8251 | 0.9633 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
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