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--- |
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language: |
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- mn |
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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: xlm-roberta-base-ner-hrl-ner-finetuning |
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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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# xlm-roberta-base-ner-hrl-ner-finetuning |
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This model is a fine-tuned version of [Davlan/xlm-roberta-base-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1135 |
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- Precision: 0.9290 |
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- Recall: 0.9367 |
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- F1: 0.9328 |
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- Accuracy: 0.9801 |
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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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| 0.1534 | 1.0 | 477 | 0.0870 | 0.9001 | 0.9124 | 0.9062 | 0.9740 | |
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| 0.077 | 2.0 | 954 | 0.0764 | 0.9187 | 0.9321 | 0.9253 | 0.9789 | |
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| 0.0529 | 3.0 | 1431 | 0.0845 | 0.9178 | 0.9313 | 0.9245 | 0.9791 | |
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| 0.0377 | 4.0 | 1908 | 0.0805 | 0.9200 | 0.9310 | 0.9255 | 0.9795 | |
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| 0.0292 | 5.0 | 2385 | 0.0918 | 0.9278 | 0.9346 | 0.9312 | 0.9795 | |
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| 0.0204 | 6.0 | 2862 | 0.1016 | 0.9222 | 0.9323 | 0.9273 | 0.9790 | |
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| 0.0167 | 7.0 | 3339 | 0.1066 | 0.9271 | 0.9327 | 0.9299 | 0.9790 | |
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| 0.0134 | 8.0 | 3816 | 0.1088 | 0.9253 | 0.9358 | 0.9305 | 0.9797 | |
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| 0.0101 | 9.0 | 4293 | 0.1134 | 0.9289 | 0.9357 | 0.9323 | 0.9798 | |
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| 0.0079 | 10.0 | 4770 | 0.1135 | 0.9290 | 0.9367 | 0.9328 | 0.9801 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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