---
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: 0.95951375991896
---

<!-- 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: 0.1422
- Precision: 0.7886
- Recall: 0.8150
- F1: 0.8016
- Accuracy: 0.9595

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 207  | 0.2017          | 0.7113    | 0.7389 | 0.7249 | 0.9419   |
| No log        | 2.0   | 414  | 0.1594          | 0.7272    | 0.7787 | 0.7521 | 0.9510   |
| 0.233         | 3.0   | 621  | 0.1476          | 0.7576    | 0.8020 | 0.7792 | 0.9560   |
| 0.233         | 4.0   | 828  | 0.1471          | 0.7782    | 0.8130 | 0.7952 | 0.9582   |
| 0.0888        | 5.0   | 1035 | 0.1422          | 0.7886    | 0.8150 | 0.8016 | 0.9595   |


### Framework versions

- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1