---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-srb-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      args: sr
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9570619691726958
---

<!-- 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

This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2532
- Precision: 0.8859
- Recall: 0.9066
- F1: 0.8962
- Accuracy: 0.9571

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2615        | 1.0   | 1250  | 0.2126          | 0.8268    | 0.8327 | 0.8297 | 0.9319   |
| 0.1568        | 2.0   | 2500  | 0.1775          | 0.8695    | 0.8699 | 0.8697 | 0.9472   |
| 0.1017        | 3.0   | 3750  | 0.1718          | 0.8649    | 0.8857 | 0.8752 | 0.9504   |
| 0.066         | 4.0   | 5000  | 0.1906          | 0.8734    | 0.8930 | 0.8831 | 0.9530   |
| 0.0413        | 5.0   | 6250  | 0.2076          | 0.8805    | 0.8992 | 0.8897 | 0.9549   |
| 0.03          | 6.0   | 7500  | 0.2257          | 0.8758    | 0.9045 | 0.8899 | 0.9554   |
| 0.0213        | 7.0   | 8750  | 0.2286          | 0.8864    | 0.9015 | 0.8939 | 0.9556   |
| 0.0157        | 8.0   | 10000 | 0.2454          | 0.8874    | 0.9021 | 0.8947 | 0.9566   |
| 0.01          | 9.0   | 11250 | 0.2486          | 0.8878    | 0.9043 | 0.8960 | 0.9573   |
| 0.0076        | 10.0  | 12500 | 0.2532          | 0.8859    | 0.9066 | 0.8962 | 0.9571   |


### Framework versions

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