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--- |
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language: |
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- mn |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: mongolian-gpt2-ner |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mongolian-gpt2-ner |
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2599 |
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- Precision: 0.1483 |
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- Recall: 0.2561 |
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- F1: 0.1878 |
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- Accuracy: 0.9149 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.4822 | 1.0 | 477 | 0.3452 | 0.1156 | 0.2072 | 0.1484 | 0.8876 | |
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| 0.3376 | 2.0 | 954 | 0.3196 | 0.1369 | 0.2304 | 0.1717 | 0.8975 | |
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| 0.3084 | 3.0 | 1431 | 0.2915 | 0.1242 | 0.2257 | 0.1603 | 0.9015 | |
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| 0.2889 | 4.0 | 1908 | 0.2800 | 0.1328 | 0.2375 | 0.1704 | 0.9063 | |
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| 0.275 | 5.0 | 2385 | 0.2734 | 0.1439 | 0.2452 | 0.1814 | 0.9099 | |
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| 0.264 | 6.0 | 2862 | 0.2691 | 0.1426 | 0.2420 | 0.1795 | 0.9115 | |
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| 0.256 | 7.0 | 3339 | 0.2639 | 0.1411 | 0.2442 | 0.1789 | 0.9129 | |
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| 0.2498 | 8.0 | 3816 | 0.2628 | 0.1482 | 0.2511 | 0.1864 | 0.9135 | |
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| 0.2438 | 9.0 | 4293 | 0.2603 | 0.1483 | 0.2548 | 0.1875 | 0.9143 | |
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| 0.2388 | 10.0 | 4770 | 0.2599 | 0.1483 | 0.2561 | 0.1878 | 0.9149 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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