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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: mongolian-ner-test-xlm-roberta-large-ner-hrl |
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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-ner-test-xlm-roberta-large-ner-hrl |
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This model is a fine-tuned version of [bayartsogt/albert-mongolian](https://huggingface.co/bayartsogt/albert-mongolian) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5337 |
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- Precision: 0.3060 |
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- Recall: 0.1406 |
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- F1: 0.1927 |
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- Accuracy: 0.8591 |
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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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| 0.6123 | 1.0 | 477 | 0.5570 | 0.2422 | 0.0999 | 0.1414 | 0.8536 | |
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| 0.5411 | 2.0 | 954 | 0.5407 | 0.2914 | 0.1294 | 0.1792 | 0.8572 | |
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| 0.5288 | 3.0 | 1431 | 0.5394 | 0.2944 | 0.1309 | 0.1812 | 0.8576 | |
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| 0.5212 | 4.0 | 1908 | 0.5346 | 0.3015 | 0.1324 | 0.1840 | 0.8581 | |
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| 0.5156 | 5.0 | 2385 | 0.5298 | 0.3131 | 0.1394 | 0.1929 | 0.8595 | |
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| 0.5103 | 6.0 | 2862 | 0.5301 | 0.3086 | 0.1419 | 0.1944 | 0.8595 | |
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| 0.5041 | 7.0 | 3339 | 0.5318 | 0.3083 | 0.1411 | 0.1936 | 0.8592 | |
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| 0.4981 | 8.0 | 3816 | 0.5308 | 0.3117 | 0.1421 | 0.1952 | 0.8595 | |
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| 0.4931 | 9.0 | 4293 | 0.5329 | 0.3062 | 0.1400 | 0.1922 | 0.8592 | |
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| 0.4885 | 10.0 | 4770 | 0.5337 | 0.3060 | 0.1406 | 0.1927 | 0.8591 | |
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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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