---
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: 0.95641898994996
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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: 0.3017
- Precision: 0.8911
- Recall: 0.9081
- F1: 0.8995
- Accuracy: 0.9564

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2535        | 1.0   | 1250  | 0.2015          | 0.8494    | 0.8605 | 0.8549 | 0.9376   |
| 0.1461        | 2.0   | 2500  | 0.1853          | 0.8800    | 0.8681 | 0.8740 | 0.9464   |
| 0.0914        | 3.0   | 3750  | 0.2022          | 0.8695    | 0.8912 | 0.8802 | 0.9485   |
| 0.0545        | 4.0   | 5000  | 0.2214          | 0.8758    | 0.8975 | 0.8865 | 0.9514   |
| 0.0385        | 5.0   | 6250  | 0.2536          | 0.8806    | 0.9010 | 0.8907 | 0.9523   |
| 0.0266        | 6.0   | 7500  | 0.2506          | 0.8834    | 0.9020 | 0.8926 | 0.9539   |
| 0.0133        | 7.0   | 8750  | 0.2745          | 0.8910    | 0.9057 | 0.8983 | 0.9562   |
| 0.0077        | 8.0   | 10000 | 0.2946          | 0.8872    | 0.9065 | 0.8968 | 0.9559   |
| 0.0043        | 9.0   | 11250 | 0.2931          | 0.8902    | 0.9094 | 0.8997 | 0.9567   |
| 0.0022        | 10.0  | 12500 | 0.3017          | 0.8911    | 0.9081 | 0.8995 | 0.9564   |


### Framework versions

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