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--- |
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language: |
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- mn |
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license: apache-2.0 |
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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: mn-cased-bert-base-named-entity |
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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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# mn-cased-bert-base-named-entity |
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1382 |
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- Precision: 0.8816 |
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- Recall: 0.9031 |
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- F1: 0.8922 |
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- Accuracy: 0.9729 |
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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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| 0.2157 | 1.0 | 477 | 0.1347 | 0.7747 | 0.8399 | 0.8060 | 0.9544 | |
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| 0.103 | 2.0 | 954 | 0.1062 | 0.8510 | 0.8853 | 0.8678 | 0.9677 | |
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| 0.0673 | 3.0 | 1431 | 0.1033 | 0.8549 | 0.8891 | 0.8717 | 0.9693 | |
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| 0.048 | 4.0 | 1908 | 0.1110 | 0.8610 | 0.8920 | 0.8762 | 0.9690 | |
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| 0.0347 | 5.0 | 2385 | 0.1175 | 0.8731 | 0.8967 | 0.8848 | 0.9715 | |
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| 0.0256 | 6.0 | 2862 | 0.1209 | 0.8741 | 0.9021 | 0.8879 | 0.9728 | |
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| 0.0183 | 7.0 | 3339 | 0.1219 | 0.8765 | 0.9004 | 0.8883 | 0.9732 | |
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| 0.0133 | 8.0 | 3816 | 0.1296 | 0.8799 | 0.9036 | 0.8916 | 0.9726 | |
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| 0.011 | 9.0 | 4293 | 0.1375 | 0.8779 | 0.9031 | 0.8903 | 0.9726 | |
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| 0.009 | 10.0 | 4770 | 0.1382 | 0.8816 | 0.9031 | 0.8922 | 0.9729 | |
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### Framework versions |
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- Transformers 4.28.0 |
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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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