---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner-setimes
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.951370041268543
---

<!-- 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-setimes

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2619
- Precision: 0.8157
- Recall: 0.7934
- F1: 0.8044
- Accuracy: 0.9514

## 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  | 0.2845          | 0.7431    | 0.6314 | 0.6827 | 0.9225   |
| No log        | 2.0   | 414  | 0.2082          | 0.7766    | 0.7134 | 0.7436 | 0.9396   |
| 0.2949        | 3.0   | 621  | 0.1992          | 0.7699    | 0.7596 | 0.7647 | 0.9439   |
| 0.2949        | 4.0   | 828  | 0.2044          | 0.7485    | 0.7908 | 0.7691 | 0.9456   |
| 0.0896        | 5.0   | 1035 | 0.2129          | 0.7827    | 0.7778 | 0.7802 | 0.9476   |
| 0.0896        | 6.0   | 1242 | 0.2330          | 0.7893    | 0.7882 | 0.7887 | 0.9485   |
| 0.0896        | 7.0   | 1449 | 0.2337          | 0.8026    | 0.7947 | 0.7986 | 0.9504   |
| 0.0334        | 8.0   | 1656 | 0.2579          | 0.8111    | 0.7850 | 0.7978 | 0.9503   |
| 0.0334        | 9.0   | 1863 | 0.2792          | 0.8263    | 0.7830 | 0.8041 | 0.9510   |
| 0.0152        | 10.0  | 2070 | 0.2619          | 0.8157    | 0.7934 | 0.8044 | 0.9514   |


### Framework versions

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