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--- |
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language: |
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- mn |
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base_model: bayartsogt/mongolian-roberta-base |
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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: roberta-base-ner-demo-turshilt2 |
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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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# roberta-base-ner-demo-turshilt2 |
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This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1242 |
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- Precision: 0.9296 |
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- Recall: 0.9365 |
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- F1: 0.9330 |
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- Accuracy: 0.9802 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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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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| 0.6386 | 0.9958 | 119 | 0.1340 | 0.7472 | 0.8012 | 0.7732 | 0.9536 | |
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| 0.1096 | 2.0 | 239 | 0.0939 | 0.8249 | 0.8791 | 0.8511 | 0.9686 | |
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| 0.0647 | 2.9958 | 358 | 0.0893 | 0.8356 | 0.8889 | 0.8614 | 0.9715 | |
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| 0.0455 | 4.0 | 478 | 0.0963 | 0.8452 | 0.8912 | 0.8676 | 0.9712 | |
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| 0.0306 | 4.9958 | 597 | 0.0909 | 0.9234 | 0.9314 | 0.9274 | 0.9795 | |
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| 0.0146 | 6.0 | 717 | 0.1046 | 0.9235 | 0.9302 | 0.9268 | 0.9789 | |
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| 0.0108 | 6.9958 | 836 | 0.1040 | 0.9204 | 0.9311 | 0.9257 | 0.9794 | |
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| 0.0079 | 8.0 | 956 | 0.1168 | 0.9245 | 0.9309 | 0.9277 | 0.9792 | |
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| 0.0063 | 8.9958 | 1075 | 0.1138 | 0.9258 | 0.9337 | 0.9297 | 0.9800 | |
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| 0.0051 | 10.0 | 1195 | 0.1165 | 0.9268 | 0.9330 | 0.9299 | 0.9800 | |
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| 0.0047 | 10.9958 | 1314 | 0.1199 | 0.9261 | 0.9359 | 0.9309 | 0.9803 | |
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| 0.0034 | 12.0 | 1434 | 0.1238 | 0.9284 | 0.9358 | 0.9321 | 0.9800 | |
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| 0.0027 | 12.9958 | 1553 | 0.1242 | 0.9267 | 0.9355 | 0.9311 | 0.9800 | |
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| 0.0026 | 14.0 | 1673 | 0.1232 | 0.9294 | 0.9365 | 0.9329 | 0.9804 | |
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| 0.0023 | 14.9372 | 1785 | 0.1242 | 0.9296 | 0.9365 | 0.9330 | 0.9802 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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