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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: testingModel |
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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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# testingModel |
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This model is a fine-tuned version of [Davlan/distilbert-base-multilingual-cased-ner-hrl](https://huggingface.co/Davlan/distilbert-base-multilingual-cased-ner-hrl) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1368 |
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- Precision: 0.8763 |
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- Recall: 0.9000 |
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- F1: 0.8880 |
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- Accuracy: 0.9738 |
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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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| 0.1975 | 1.0 | 477 | 0.1150 | 0.8257 | 0.8574 | 0.8412 | 0.9642 | |
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| 0.1001 | 2.0 | 954 | 0.1046 | 0.8515 | 0.8798 | 0.8654 | 0.9682 | |
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| 0.0655 | 3.0 | 1431 | 0.0980 | 0.8632 | 0.8905 | 0.8766 | 0.9719 | |
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| 0.0453 | 4.0 | 1908 | 0.1088 | 0.8590 | 0.8944 | 0.8763 | 0.9718 | |
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| 0.0324 | 5.0 | 2385 | 0.1142 | 0.8673 | 0.8951 | 0.8810 | 0.9719 | |
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| 0.0223 | 6.0 | 2862 | 0.1244 | 0.8814 | 0.9036 | 0.8924 | 0.9737 | |
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| 0.0173 | 7.0 | 3339 | 0.1252 | 0.8739 | 0.9007 | 0.8871 | 0.9733 | |
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| 0.0131 | 8.0 | 3816 | 0.1328 | 0.8721 | 0.8965 | 0.8841 | 0.9731 | |
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| 0.0097 | 9.0 | 4293 | 0.1362 | 0.8783 | 0.9002 | 0.8891 | 0.9737 | |
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| 0.008 | 10.0 | 4770 | 0.1368 | 0.8763 | 0.9000 | 0.8880 | 0.9738 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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