---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-srb-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      args: sr
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9542715764169646
---

<!-- 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

This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3045
- Precision: 0.8922
- Recall: 0.9050
- F1: 0.8986
- Accuracy: 0.9543

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.276         | 1.0   | 1250  | 0.2359          | 0.8355    | 0.8334 | 0.8344 | 0.9276   |
| 0.1722        | 2.0   | 2500  | 0.2016          | 0.8731    | 0.8685 | 0.8708 | 0.9426   |
| 0.1155        | 3.0   | 3750  | 0.1897          | 0.8707    | 0.8860 | 0.8783 | 0.9463   |
| 0.0849        | 4.0   | 5000  | 0.2151          | 0.8755    | 0.8980 | 0.8866 | 0.9494   |
| 0.0554        | 5.0   | 6250  | 0.2373          | 0.8820    | 0.8923 | 0.8871 | 0.9495   |
| 0.039         | 6.0   | 7500  | 0.2644          | 0.8808    | 0.8953 | 0.8880 | 0.9505   |
| 0.0286        | 7.0   | 8750  | 0.2737          | 0.8915    | 0.8961 | 0.8938 | 0.9520   |
| 0.018         | 8.0   | 10000 | 0.2879          | 0.8860    | 0.9039 | 0.8948 | 0.9526   |
| 0.0116        | 9.0   | 11250 | 0.2973          | 0.8930    | 0.9032 | 0.8981 | 0.9542   |
| 0.0079        | 10.0  | 12500 | 0.3045          | 0.8922    | 0.9050 | 0.8986 | 0.9543   |


### Framework versions

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