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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-uncased
model-index:
- name: bert-base-uncased-tajik-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: wikiann
type: wikiann
config: tg
split: train+test
args: tg
metrics:
- type: precision
value: 0.5042016806722689
name: Precision
- type: recall
value: 0.5769230769230769
name: Recall
- type: f1
value: 0.5381165919282511
name: F1
- type: accuracy
value: 0.848129958443521
name: Accuracy
---
<!-- 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
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