---
widget:
- text: " Исмоили Сомонӣ - намояндаи бузурги форсу-тоҷик" 
- text: "Ин фурудгоҳ дар кишвари Индонезия қарор дорад."
- text: " Бобоҷон Ғафуров – солҳои 1946-1956"
- text: " Лоиқ Шералӣ дар васфи Модар шеър"

tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tajberto-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: tg
      split: train+test
      args: tg
    metrics:
    - name: Precision
      type: precision
      value: 0.576
    - name: Recall
      type: recall
      value: 0.6923076923076923
    - name: F1
      type: f1
      value: 0.62882096069869
    - name: Accuracy
      type: accuracy
      value: 0.8934049079754601
---

<!-- 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. -->

# tajberto-ner

This model is a fine-tuned version of [muhtasham/TajBERTo](https://huggingface.co/muhtasham/TajBERTo) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6129
- Precision: 0.576
- Recall: 0.6923
- F1: 0.6288
- Accuracy: 0.8934

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 50   | 0.6171          | 0.1667    | 0.2885 | 0.2113 | 0.7646   |
| No log        | 4.0   | 100  | 0.4733          | 0.2824    | 0.4615 | 0.3504 | 0.8344   |
| No log        | 6.0   | 150  | 0.3857          | 0.3372    | 0.5577 | 0.4203 | 0.8589   |
| No log        | 8.0   | 200  | 0.4523          | 0.4519    | 0.5865 | 0.5105 | 0.8765   |
| No log        | 10.0  | 250  | 0.3870          | 0.44      | 0.6346 | 0.5197 | 0.8834   |
| No log        | 12.0  | 300  | 0.4512          | 0.5267    | 0.6635 | 0.5872 | 0.8865   |
| No log        | 14.0  | 350  | 0.4934          | 0.4789    | 0.6538 | 0.5528 | 0.8819   |
| No log        | 16.0  | 400  | 0.4924          | 0.4783    | 0.6346 | 0.5455 | 0.8842   |
| No log        | 18.0  | 450  | 0.5355          | 0.4595    | 0.6538 | 0.5397 | 0.8788   |
| 0.1682        | 20.0  | 500  | 0.5440          | 0.5547    | 0.6827 | 0.6121 | 0.8942   |
| 0.1682        | 22.0  | 550  | 0.5299          | 0.5794    | 0.7019 | 0.6348 | 0.9003   |
| 0.1682        | 24.0  | 600  | 0.5735          | 0.5691    | 0.6731 | 0.6167 | 0.8926   |
| 0.1682        | 26.0  | 650  | 0.6027          | 0.5833    | 0.6731 | 0.6250 | 0.8796   |
| 0.1682        | 28.0  | 700  | 0.6119          | 0.568     | 0.6827 | 0.6201 | 0.8934   |
| 0.1682        | 30.0  | 750  | 0.6098          | 0.5635    | 0.6827 | 0.6174 | 0.8911   |
| 0.1682        | 32.0  | 800  | 0.6237          | 0.5469    | 0.6731 | 0.6034 | 0.8834   |
| 0.1682        | 34.0  | 850  | 0.6215          | 0.5530    | 0.7019 | 0.6186 | 0.8842   |
| 0.1682        | 36.0  | 900  | 0.6179          | 0.5802    | 0.7308 | 0.6468 | 0.8888   |
| 0.1682        | 38.0  | 950  | 0.6201          | 0.5373    | 0.6923 | 0.6050 | 0.8873   |
| 0.0007        | 40.0  | 1000 | 0.6114          | 0.5952    | 0.7212 | 0.6522 | 0.8911   |
| 0.0007        | 42.0  | 1050 | 0.6073          | 0.5625    | 0.6923 | 0.6207 | 0.8896   |
| 0.0007        | 44.0  | 1100 | 0.6327          | 0.5620    | 0.6538 | 0.6044 | 0.8896   |
| 0.0007        | 46.0  | 1150 | 0.6129          | 0.576     | 0.6923 | 0.6288 | 0.8934   |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1