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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: NLP_91_1 |
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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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# NLP_91_1 |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co./google-bert/bert-base-multilingual-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4408 |
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- Accuracy: 0.9220 |
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- Precision: 0.9156 |
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- Recall: 0.9170 |
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- F1: 0.9158 |
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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: 1e-05 |
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- train_batch_size: 32 |
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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: cosine |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.309 | 1.0 | 48 | 0.4280 | 0.8532 | 0.8506 | 0.8461 | 0.8454 | |
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| 0.2595 | 2.0 | 96 | 0.4335 | 0.8807 | 0.8767 | 0.8766 | 0.8738 | |
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| 0.2196 | 3.0 | 144 | 0.3883 | 0.8945 | 0.8956 | 0.8869 | 0.8876 | |
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| 0.1812 | 4.0 | 192 | 0.4664 | 0.8761 | 0.8856 | 0.8614 | 0.8638 | |
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| 0.1256 | 5.0 | 240 | 0.4764 | 0.8670 | 0.8750 | 0.8627 | 0.8625 | |
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| 0.142 | 6.0 | 288 | 0.5258 | 0.8670 | 0.8818 | 0.8580 | 0.8607 | |
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| 0.1006 | 7.0 | 336 | 0.4323 | 0.9037 | 0.8961 | 0.8989 | 0.8970 | |
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| 0.0897 | 8.0 | 384 | 0.4659 | 0.8991 | 0.8959 | 0.8891 | 0.8914 | |
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| 0.0595 | 9.0 | 432 | 0.4569 | 0.9174 | 0.9149 | 0.9099 | 0.9115 | |
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| 0.0399 | 10.0 | 480 | 0.4592 | 0.9037 | 0.8981 | 0.8970 | 0.8966 | |
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| 0.056 | 11.0 | 528 | 0.4461 | 0.9174 | 0.9102 | 0.9091 | 0.9094 | |
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| 0.0451 | 12.0 | 576 | 0.4772 | 0.8991 | 0.8926 | 0.8891 | 0.8906 | |
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| 0.0309 | 13.0 | 624 | 0.4396 | 0.9220 | 0.9160 | 0.9169 | 0.9155 | |
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| 0.0338 | 14.0 | 672 | 0.4423 | 0.9220 | 0.9156 | 0.9170 | 0.9158 | |
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| 0.0458 | 15.0 | 720 | 0.4408 | 0.9220 | 0.9156 | 0.9170 | 0.9158 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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