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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: bert-base-multilingual-cased-ner-demo |
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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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# bert-base-multilingual-cased-ner-demo |
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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: 0.1333 |
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- Precision: 0.9160 |
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- Recall: 0.9229 |
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- F1: 0.9194 |
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- Accuracy: 0.9779 |
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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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| 0.1658 | 1.0 | 572 | 0.1113 | 0.8581 | 0.8792 | 0.8685 | 0.9658 | |
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| 0.0816 | 2.0 | 1144 | 0.0880 | 0.8950 | 0.9096 | 0.9022 | 0.9737 | |
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| 0.0554 | 3.0 | 1716 | 0.0935 | 0.8941 | 0.9096 | 0.9018 | 0.9741 | |
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| 0.0394 | 4.0 | 2288 | 0.1069 | 0.9070 | 0.9189 | 0.9129 | 0.9762 | |
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| 0.0284 | 5.0 | 2860 | 0.1029 | 0.9007 | 0.9184 | 0.9095 | 0.9752 | |
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| 0.0192 | 6.0 | 3432 | 0.1110 | 0.9102 | 0.9214 | 0.9157 | 0.9764 | |
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| 0.0139 | 7.0 | 4004 | 0.1156 | 0.9166 | 0.9272 | 0.9218 | 0.9786 | |
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| 0.0095 | 8.0 | 4576 | 0.1319 | 0.9091 | 0.9174 | 0.9132 | 0.9761 | |
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| 0.0066 | 9.0 | 5148 | 0.1313 | 0.9132 | 0.9226 | 0.9179 | 0.9781 | |
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| 0.0053 | 10.0 | 5720 | 0.1333 | 0.9160 | 0.9229 | 0.9194 | 0.9779 | |
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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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