---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9509610737256943
---

<!-- 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. -->

# electra-srb-ner

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2686
- Precision: 0.8132
- Recall: 0.7889
- F1: 0.8009
- Accuracy: 0.9510

## 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  | 0.2887          | 0.7392    | 0.6269 | 0.6784 | 0.9221   |
| No log        | 2.0   | 414  | 0.2076          | 0.7690    | 0.7147 | 0.7409 | 0.9397   |
| 0.2949        | 3.0   | 621  | 0.2011          | 0.7698    | 0.7583 | 0.7640 | 0.9441   |
| 0.2949        | 4.0   | 828  | 0.2077          | 0.7600    | 0.7807 | 0.7702 | 0.9451   |
| 0.089         | 5.0   | 1035 | 0.2198          | 0.7884    | 0.7684 | 0.7783 | 0.9465   |
| 0.089         | 6.0   | 1242 | 0.2437          | 0.7885    | 0.7824 | 0.7854 | 0.9474   |
| 0.089         | 7.0   | 1449 | 0.2394          | 0.7986    | 0.7970 | 0.7978 | 0.9511   |
| 0.0322        | 8.0   | 1656 | 0.2675          | 0.8135    | 0.7775 | 0.7951 | 0.9497   |
| 0.0322        | 9.0   | 1863 | 0.2832          | 0.8161    | 0.7798 | 0.7975 | 0.9508   |
| 0.0141        | 10.0  | 2070 | 0.2686          | 0.8132    | 0.7889 | 0.8009 | 0.9510   |


### Framework versions

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