---
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