---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-srb-ner-setimes
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9660941783583293
---

<!-- 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. -->

# distilbert-srb-ner-setimes

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1522
- Precision: 0.8280
- Recall: 0.8607
- F1: 0.8440
- Accuracy: 0.9661

## 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  | 0.2240          | 0.6996    | 0.7200 | 0.7097 | 0.9375   |
| No log        | 2.0   | 414  | 0.1538          | 0.7501    | 0.7930 | 0.7710 | 0.9546   |
| 0.2348        | 3.0   | 621  | 0.1459          | 0.7756    | 0.8115 | 0.7931 | 0.9576   |
| 0.2348        | 4.0   | 828  | 0.1465          | 0.7918    | 0.8456 | 0.8178 | 0.9611   |
| 0.0782        | 5.0   | 1035 | 0.1310          | 0.7981    | 0.8352 | 0.8162 | 0.9636   |
| 0.0782        | 6.0   | 1242 | 0.1466          | 0.8103    | 0.8510 | 0.8301 | 0.9646   |
| 0.0782        | 7.0   | 1449 | 0.1441          | 0.8222    | 0.8503 | 0.8360 | 0.9655   |
| 0.0343        | 8.0   | 1656 | 0.1493          | 0.8265    | 0.8600 | 0.8429 | 0.9666   |
| 0.0343        | 9.0   | 1863 | 0.1524          | 0.8236    | 0.8570 | 0.8400 | 0.9656   |
| 0.0169        | 10.0  | 2070 | 0.1522          | 0.8280    | 0.8607 | 0.8440 | 0.9661   |


### Framework versions

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