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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-turshilt3 |
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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-turshilt3 |
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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: 0.1163 |
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- Precision: 0.9235 |
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- Recall: 0.9346 |
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- F1: 0.9290 |
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- Accuracy: 0.9806 |
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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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| 0.78 | 0.9958 | 119 | 0.1344 | 0.7247 | 0.8054 | 0.7629 | 0.9518 | |
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| 0.1075 | 2.0 | 239 | 0.0992 | 0.8035 | 0.8679 | 0.8344 | 0.9656 | |
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| 0.0642 | 2.9958 | 358 | 0.0831 | 0.8306 | 0.8849 | 0.8569 | 0.9714 | |
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| 0.0412 | 4.0 | 478 | 0.0924 | 0.8641 | 0.9022 | 0.8827 | 0.9739 | |
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| 0.0214 | 4.9958 | 597 | 0.0918 | 0.9064 | 0.9225 | 0.9144 | 0.9778 | |
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| 0.0143 | 6.0 | 717 | 0.0932 | 0.9189 | 0.9301 | 0.9245 | 0.9801 | |
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| 0.01 | 6.9958 | 836 | 0.0951 | 0.9199 | 0.9325 | 0.9261 | 0.9803 | |
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| 0.0074 | 8.0 | 956 | 0.1077 | 0.9207 | 0.9299 | 0.9253 | 0.9795 | |
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| 0.0053 | 8.9958 | 1075 | 0.1081 | 0.9213 | 0.9329 | 0.9270 | 0.9805 | |
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| 0.0044 | 10.0 | 1195 | 0.1110 | 0.9223 | 0.9331 | 0.9276 | 0.9806 | |
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| 0.0037 | 10.9958 | 1314 | 0.1125 | 0.9273 | 0.9362 | 0.9317 | 0.9811 | |
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| 0.0027 | 12.0 | 1434 | 0.1146 | 0.9250 | 0.9344 | 0.9297 | 0.9807 | |
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| 0.0023 | 12.9958 | 1553 | 0.1155 | 0.9263 | 0.9361 | 0.9311 | 0.9812 | |
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| 0.0023 | 14.0 | 1673 | 0.1171 | 0.9242 | 0.9342 | 0.9292 | 0.9805 | |
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| 0.0021 | 14.9372 | 1785 | 0.1163 | 0.9235 | 0.9346 | 0.9290 | 0.9806 | |
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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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