---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-small-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: 0.43884892086330934
    - name: Recall
      type: recall
      value: 0.5865384615384616
    - name: F1
      type: f1
      value: 0.5020576131687243
    - name: Accuracy
      type: accuracy
      value: 0.8269739327540612
---

<!-- 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-small-uncased-tajik-ner

This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co./google/bert_uncased_L-4_H-512_A-8) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1663
- Precision: 0.4388
- Recall: 0.5865
- F1: 0.5021
- Accuracy: 0.8270

## 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   | 1.1113          | 0.0238    | 0.0481 | 0.0318 | 0.5984   |
| No log        | 4.0   | 100  | 0.9179          | 0.0976    | 0.1538 | 0.1194 | 0.6547   |
| No log        | 6.0   | 150  | 0.9254          | 0.08      | 0.1538 | 0.1053 | 0.6634   |
| No log        | 8.0   | 200  | 0.6607          | 0.1299    | 0.2212 | 0.1637 | 0.7707   |
| No log        | 10.0  | 250  | 0.6514          | 0.2583    | 0.375  | 0.3059 | 0.7896   |
| No log        | 12.0  | 300  | 0.6213          | 0.2836    | 0.3654 | 0.3193 | 0.8058   |
| No log        | 14.0  | 350  | 0.6696          | 0.3611    | 0.5    | 0.4194 | 0.8100   |
| No log        | 16.0  | 400  | 0.7094          | 0.3893    | 0.4904 | 0.4340 | 0.8187   |
| No log        | 18.0  | 450  | 0.7557          | 0.38      | 0.5481 | 0.4488 | 0.8243   |
| 0.5061        | 20.0  | 500  | 0.7409          | 0.4222    | 0.5481 | 0.4770 | 0.8342   |
| 0.5061        | 22.0  | 550  | 0.8003          | 0.4196    | 0.5769 | 0.4858 | 0.8349   |
| 0.5061        | 24.0  | 600  | 0.8173          | 0.4275    | 0.5673 | 0.4876 | 0.8342   |
| 0.5061        | 26.0  | 650  | 0.7942          | 0.4225    | 0.5769 | 0.4878 | 0.8323   |
| 0.5061        | 28.0  | 700  | 0.8565          | 0.4067    | 0.5865 | 0.4803 | 0.8281   |
| 0.5061        | 30.0  | 750  | 0.8040          | 0.4388    | 0.5865 | 0.5021 | 0.8406   |
| 0.5061        | 32.0  | 800  | 0.9251          | 0.4286    | 0.5769 | 0.4918 | 0.8368   |
| 0.5061        | 34.0  | 850  | 0.8421          | 0.4196    | 0.5769 | 0.4858 | 0.8394   |
| 0.5061        | 36.0  | 900  | 0.8608          | 0.4207    | 0.5865 | 0.4900 | 0.8330   |
| 0.5061        | 38.0  | 950  | 0.8622          | 0.5333    | 0.6154 | 0.5714 | 0.8489   |
| 0.0304        | 40.0  | 1000 | 0.9901          | 0.4306    | 0.5962 | 0.5000 | 0.8240   |
| 0.0304        | 42.0  | 1050 | 0.9677          | 0.4286    | 0.6058 | 0.5020 | 0.8345   |
| 0.0304        | 44.0  | 1100 | 0.9203          | 0.4429    | 0.5962 | 0.5082 | 0.8440   |
| 0.0304        | 46.0  | 1150 | 0.9368          | 0.4559    | 0.5962 | 0.5167 | 0.8428   |
| 0.0304        | 48.0  | 1200 | 0.9747          | 0.4420    | 0.5865 | 0.5041 | 0.8342   |
| 0.0304        | 50.0  | 1250 | 0.9033          | 0.4266    | 0.5865 | 0.4939 | 0.8360   |
| 0.0304        | 52.0  | 1300 | 0.9242          | 0.4806    | 0.5962 | 0.5322 | 0.8519   |
| 0.0304        | 54.0  | 1350 | 0.9496          | 0.4150    | 0.5865 | 0.4861 | 0.8406   |
| 0.0304        | 56.0  | 1400 | 1.0157          | 0.4388    | 0.5865 | 0.5021 | 0.8274   |
| 0.0304        | 58.0  | 1450 | 1.0069          | 0.3789    | 0.5865 | 0.4604 | 0.8357   |
| 0.0041        | 60.0  | 1500 | 1.0159          | 0.4593    | 0.5962 | 0.5188 | 0.8413   |
| 0.0041        | 62.0  | 1550 | 1.0138          | 0.488     | 0.5865 | 0.5328 | 0.8428   |
| 0.0041        | 64.0  | 1600 | 1.0406          | 0.4526    | 0.5962 | 0.5145 | 0.8398   |
| 0.0041        | 66.0  | 1650 | 1.0672          | 0.504     | 0.6058 | 0.5502 | 0.8413   |
| 0.0041        | 68.0  | 1700 | 1.0713          | 0.4257    | 0.6058 | 0.5    | 0.8334   |
| 0.0041        | 70.0  | 1750 | 1.0001          | 0.5079    | 0.6154 | 0.5565 | 0.8515   |
| 0.0041        | 72.0  | 1800 | 0.9986          | 0.4632    | 0.6058 | 0.525  | 0.8451   |
| 0.0041        | 74.0  | 1850 | 1.0523          | 0.4643    | 0.625  | 0.5328 | 0.8357   |
| 0.0041        | 76.0  | 1900 | 1.1331          | 0.4437    | 0.6058 | 0.5122 | 0.8281   |
| 0.0041        | 78.0  | 1950 | 1.0217          | 0.4667    | 0.6058 | 0.5272 | 0.8406   |
| 0.0023        | 80.0  | 2000 | 1.0296          | 0.4519    | 0.5865 | 0.5105 | 0.8372   |
| 0.0023        | 82.0  | 2050 | 1.0603          | 0.5207    | 0.6058 | 0.56   | 0.8512   |
| 0.0023        | 84.0  | 2100 | 1.1181          | 0.4733    | 0.5962 | 0.5277 | 0.8319   |
| 0.0023        | 86.0  | 2150 | 1.0858          | 0.4701    | 0.6058 | 0.5294 | 0.8383   |
| 0.0023        | 88.0  | 2200 | 1.0947          | 0.4779    | 0.625  | 0.5417 | 0.8394   |
| 0.0023        | 90.0  | 2250 | 1.0671          | 0.4539    | 0.6154 | 0.5224 | 0.8391   |
| 0.0023        | 92.0  | 2300 | 1.0958          | 0.4444    | 0.6154 | 0.5161 | 0.8372   |
| 0.0023        | 94.0  | 2350 | 1.1221          | 0.4397    | 0.5962 | 0.5061 | 0.8319   |
| 0.0023        | 96.0  | 2400 | 1.0861          | 0.5       | 0.6058 | 0.5478 | 0.8508   |
| 0.0023        | 98.0  | 2450 | 1.1522          | 0.4545    | 0.5769 | 0.5085 | 0.8258   |
| 0.0015        | 100.0 | 2500 | 1.1426          | 0.4688    | 0.5769 | 0.5172 | 0.8304   |
| 0.0015        | 102.0 | 2550 | 1.1663          | 0.4388    | 0.5865 | 0.5021 | 0.8270   |


### Framework versions

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