---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-tajik-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: 0.5042016806722689
    - name: Recall
      type: recall
      value: 0.5769230769230769
    - name: F1
      type: f1
      value: 0.5381165919282511
    - name: Accuracy
      type: accuracy
      value: 0.848129958443521
---

<!-- 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. -->

# bert-base-uncased-tajik-ner

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2137
- Precision: 0.5042
- Recall: 0.5769
- F1: 0.5381
- Accuracy: 0.8481

## 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   | 0.9499          | 0.0450    | 0.0962 | 0.0613 | 0.6626   |
| No log        | 4.0   | 100  | 0.7348          | 0.1549    | 0.2115 | 0.1789 | 0.7401   |
| No log        | 6.0   | 150  | 0.6685          | 0.1916    | 0.3077 | 0.2362 | 0.8017   |
| No log        | 8.0   | 200  | 0.7875          | 0.3923    | 0.4904 | 0.4359 | 0.8036   |
| No log        | 10.0  | 250  | 0.7495          | 0.4225    | 0.5769 | 0.4878 | 0.8274   |
| No log        | 12.0  | 300  | 0.8934          | 0.4198    | 0.5288 | 0.4681 | 0.8085   |
| No log        | 14.0  | 350  | 0.9455          | 0.4758    | 0.5673 | 0.5175 | 0.8236   |
| No log        | 16.0  | 400  | 0.9469          | 0.5893    | 0.6346 | 0.6111 | 0.8410   |
| No log        | 18.0  | 450  | 0.9936          | 0.5333    | 0.6154 | 0.5714 | 0.8485   |
| 0.2726        | 20.0  | 500  | 0.9804          | 0.5       | 0.6058 | 0.5478 | 0.8519   |
| 0.2726        | 22.0  | 550  | 1.1035          | 0.5963    | 0.625  | 0.6103 | 0.8432   |
| 0.2726        | 24.0  | 600  | 1.0318          | 0.5856    | 0.625  | 0.6047 | 0.8576   |
| 0.2726        | 26.0  | 650  | 1.1820          | 0.4921    | 0.5962 | 0.5391 | 0.8221   |
| 0.2726        | 28.0  | 700  | 1.1204          | 0.4878    | 0.5769 | 0.5286 | 0.8311   |
| 0.2726        | 30.0  | 750  | 1.1911          | 0.5357    | 0.5769 | 0.5556 | 0.8376   |
| 0.2726        | 32.0  | 800  | 1.1747          | 0.5259    | 0.5865 | 0.5545 | 0.8394   |
| 0.2726        | 34.0  | 850  | 1.1403          | 0.5872    | 0.6154 | 0.6009 | 0.8542   |
| 0.2726        | 36.0  | 900  | 1.1824          | 0.5370    | 0.5577 | 0.5472 | 0.8330   |
| 0.2726        | 38.0  | 950  | 1.1467          | 0.5424    | 0.6154 | 0.5766 | 0.8440   |
| 0.003         | 40.0  | 1000 | 1.2148          | 0.5268    | 0.5673 | 0.5463 | 0.8360   |
| 0.003         | 42.0  | 1050 | 1.3478          | 0.5273    | 0.5577 | 0.5421 | 0.8266   |
| 0.003         | 44.0  | 1100 | 1.2137          | 0.5042    | 0.5769 | 0.5381 | 0.8481   |


### Framework versions

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