README.md

---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      args: sr
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9500777931962491
---

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# electra-srb-ner

This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1859
- Precision: 0.8742
- Recall: 0.8907
- F1: 0.8824
- Accuracy: 0.9501

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3627        | 1.0   | 625  | 0.2077          | 0.8382    | 0.8545 | 0.8463 | 0.9349   |
| 0.1894        | 2.0   | 1250 | 0.1764          | 0.8640    | 0.8760 | 0.8700 | 0.9453   |
| 0.1326        | 3.0   | 1875 | 0.1848          | 0.8618    | 0.8873 | 0.8744 | 0.9473   |
| 0.0712        | 4.0   | 2500 | 0.1859          | 0.8742    | 0.8907 | 0.8824 | 0.9501   |


### Framework versions

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