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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-bert-base-demo-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-bert-base-demo-named-entity |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-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: 0.1468 |
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- Precision: 0.9092 |
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- Recall: 0.9187 |
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- F1: 0.9139 |
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- Accuracy: 0.9757 |
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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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| 0.172 | 1.0 | 477 | 0.1117 | 0.8614 | 0.8811 | 0.8711 | 0.9662 | |
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| 0.0846 | 2.0 | 954 | 0.1033 | 0.8748 | 0.8957 | 0.8852 | 0.9698 | |
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| 0.0562 | 3.0 | 1431 | 0.1005 | 0.8808 | 0.9024 | 0.8915 | 0.9716 | |
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| 0.0398 | 4.0 | 1908 | 0.1105 | 0.8978 | 0.9073 | 0.9025 | 0.9731 | |
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| 0.0276 | 5.0 | 2385 | 0.1181 | 0.9031 | 0.9121 | 0.9076 | 0.9740 | |
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| 0.0204 | 6.0 | 2862 | 0.1309 | 0.9039 | 0.9153 | 0.9096 | 0.9747 | |
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| 0.0138 | 7.0 | 3339 | 0.1322 | 0.9023 | 0.9132 | 0.9077 | 0.9745 | |
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| 0.0103 | 8.0 | 3816 | 0.1434 | 0.9071 | 0.9153 | 0.9112 | 0.9748 | |
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| 0.0071 | 9.0 | 4293 | 0.1458 | 0.9041 | 0.9156 | 0.9098 | 0.9750 | |
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| 0.0052 | 10.0 | 4770 | 0.1468 | 0.9092 | 0.9187 | 0.9139 | 0.9757 | |
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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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