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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 |
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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 |
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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: 0.1566 |
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- Precision: 0.6857 |
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- Recall: 0.7725 |
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- F1: 0.7265 |
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- Accuracy: 0.9453 |
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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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| 0.9745 | 1.0 | 477 | 0.5080 | 0.2164 | 0.1205 | 0.1548 | 0.8187 | |
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| 0.425 | 2.0 | 954 | 0.3128 | 0.5213 | 0.5929 | 0.5548 | 0.9038 | |
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| 0.2943 | 3.0 | 1431 | 0.2337 | 0.5905 | 0.6781 | 0.6313 | 0.9237 | |
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| 0.2393 | 4.0 | 1908 | 0.2000 | 0.6303 | 0.7224 | 0.6732 | 0.9333 | |
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| 0.2134 | 5.0 | 2385 | 0.1813 | 0.6526 | 0.7434 | 0.6951 | 0.9384 | |
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| 0.1978 | 6.0 | 2862 | 0.1704 | 0.6629 | 0.7527 | 0.7050 | 0.9412 | |
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| 0.1885 | 7.0 | 3339 | 0.1647 | 0.6737 | 0.7625 | 0.7154 | 0.9429 | |
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| 0.1823 | 8.0 | 3816 | 0.1595 | 0.6816 | 0.7680 | 0.7222 | 0.9443 | |
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| 0.1792 | 9.0 | 4293 | 0.1576 | 0.6843 | 0.7713 | 0.7252 | 0.9451 | |
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| 0.1778 | 10.0 | 4770 | 0.1566 | 0.6857 | 0.7725 | 0.7265 | 0.9453 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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