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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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