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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_90_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_90_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: 0.3325 |
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- Accuracy: 0.9174 |
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- Precision: 0.9126 |
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- Recall: 0.9140 |
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- F1: 0.9128 |
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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: 8 |
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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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| 0.3664 | 1.0 | 48 | 0.3609 | 0.8991 | 0.8935 | 0.8988 | 0.8938 | |
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| 0.2282 | 2.0 | 96 | 0.3376 | 0.8991 | 0.8920 | 0.8978 | 0.8927 | |
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| 0.1638 | 3.0 | 144 | 0.3184 | 0.9128 | 0.9070 | 0.9079 | 0.9070 | |
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| 0.1595 | 4.0 | 192 | 0.3291 | 0.9174 | 0.9147 | 0.9131 | 0.9135 | |
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| 0.1388 | 5.0 | 240 | 0.3495 | 0.8945 | 0.8844 | 0.8918 | 0.8865 | |
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| 0.1075 | 6.0 | 288 | 0.3357 | 0.9174 | 0.9151 | 0.9141 | 0.9139 | |
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| 0.1073 | 7.0 | 336 | 0.3311 | 0.9174 | 0.9126 | 0.9140 | 0.9128 | |
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| 0.1507 | 8.0 | 384 | 0.3325 | 0.9174 | 0.9126 | 0.9140 | 0.9128 | |
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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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