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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-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: 0.512396694214876
- name: Recall
type: recall
value: 0.5961538461538461
- name: F1
type: f1
value: 0.5511111111111111
- name: Accuracy
type: accuracy
value: 0.8520825223822499
---
<!-- 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-cased-tajik-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1137
- Precision: 0.5124
- Recall: 0.5962
- F1: 0.5511
- Accuracy: 0.8521
## 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 | 0.8416 | 0.0739 | 0.125 | 0.0929 | 0.6948 |
| No log | 4.0 | 100 | 0.7061 | 0.2229 | 0.3558 | 0.2741 | 0.7415 |
| No log | 6.0 | 150 | 0.6467 | 0.3057 | 0.4615 | 0.3678 | 0.8167 |
| No log | 8.0 | 200 | 0.7923 | 0.3968 | 0.4808 | 0.4348 | 0.8073 |
| No log | 10.0 | 250 | 0.7003 | 0.4656 | 0.5865 | 0.5191 | 0.8653 |
| No log | 12.0 | 300 | 0.7723 | 0.4380 | 0.5769 | 0.4979 | 0.8560 |
| No log | 14.0 | 350 | 0.9088 | 0.4762 | 0.5769 | 0.5217 | 0.8470 |
| No log | 16.0 | 400 | 0.9756 | 0.472 | 0.5673 | 0.5153 | 0.8424 |
| No log | 18.0 | 450 | 1.1114 | 0.4576 | 0.5192 | 0.4865 | 0.8151 |
| 0.2358 | 20.0 | 500 | 1.0887 | 0.48 | 0.5769 | 0.5240 | 0.8330 |
| 0.2358 | 22.0 | 550 | 1.0968 | 0.4419 | 0.5481 | 0.4893 | 0.8268 |
| 0.2358 | 24.0 | 600 | 1.3330 | 0.5140 | 0.5288 | 0.5213 | 0.8042 |
| 0.2358 | 26.0 | 650 | 1.0911 | 0.6019 | 0.5962 | 0.5990 | 0.8521 |
| 0.2358 | 28.0 | 700 | 1.1949 | 0.4586 | 0.5865 | 0.5148 | 0.8388 |
| 0.2358 | 30.0 | 750 | 1.1208 | 0.4444 | 0.5769 | 0.5021 | 0.8470 |
| 0.2358 | 32.0 | 800 | 1.0968 | 0.5413 | 0.5673 | 0.5540 | 0.8661 |
| 0.2358 | 34.0 | 850 | 1.1618 | 0.5 | 0.5769 | 0.5357 | 0.8575 |
| 0.2358 | 36.0 | 900 | 1.1018 | 0.5169 | 0.5865 | 0.5495 | 0.8505 |
| 0.2358 | 38.0 | 950 | 1.1948 | 0.4797 | 0.5673 | 0.5198 | 0.8431 |
| 0.0039 | 40.0 | 1000 | 1.1063 | 0.4511 | 0.5769 | 0.5063 | 0.8533 |
| 0.0039 | 42.0 | 1050 | 1.0651 | 0.5702 | 0.625 | 0.5963 | 0.8723 |
| 0.0039 | 44.0 | 1100 | 1.1475 | 0.472 | 0.5673 | 0.5153 | 0.8466 |
| 0.0039 | 46.0 | 1150 | 1.3080 | 0.4590 | 0.5385 | 0.4956 | 0.8353 |
| 0.0039 | 48.0 | 1200 | 1.1165 | 0.5741 | 0.5962 | 0.5849 | 0.8610 |
| 0.0039 | 50.0 | 1250 | 1.2525 | 0.4724 | 0.5769 | 0.5195 | 0.8431 |
| 0.0039 | 52.0 | 1300 | 1.2443 | 0.5161 | 0.6154 | 0.5614 | 0.8521 |
| 0.0039 | 54.0 | 1350 | 1.5720 | 0.4597 | 0.5481 | 0.5 | 0.8054 |
| 0.0039 | 56.0 | 1400 | 1.2487 | 0.5446 | 0.5865 | 0.5648 | 0.8513 |
| 0.0039 | 58.0 | 1450 | 1.3936 | 0.4754 | 0.5577 | 0.5133 | 0.8365 |
| 0.0051 | 60.0 | 1500 | 1.2980 | 0.5636 | 0.5962 | 0.5794 | 0.8544 |
| 0.0051 | 62.0 | 1550 | 1.3284 | 0.5175 | 0.5673 | 0.5413 | 0.8490 |
| 0.0051 | 64.0 | 1600 | 1.3345 | 0.5268 | 0.5673 | 0.5463 | 0.8447 |
| 0.0051 | 66.0 | 1650 | 1.1006 | 0.5872 | 0.6154 | 0.6009 | 0.8641 |
| 0.0051 | 68.0 | 1700 | 1.0886 | 0.4580 | 0.5769 | 0.5106 | 0.8525 |
| 0.0051 | 70.0 | 1750 | 1.1017 | 0.4959 | 0.5865 | 0.5374 | 0.8525 |
| 0.0051 | 72.0 | 1800 | 1.1137 | 0.5124 | 0.5962 | 0.5511 | 0.8521 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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