| | --- |
| | 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 |
| |
|
