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--- |
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license: apache-2.0 |
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base_model: distilbert-base-multilingual-cased |
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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: results |
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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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# results |
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-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: 1.5117 |
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- Accuracy: 0.7169 |
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- Precision: 0.6803 |
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- Recall: 0.6642 |
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- F1: 0.6693 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 999 |
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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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| 0.7606 | 1.0 | 761 | 0.6333 | 0.7228 | 0.6856 | 0.6831 | 0.6819 | |
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| 0.4448 | 2.0 | 1522 | 0.6450 | 0.7320 | 0.6983 | 0.7065 | 0.7011 | |
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| 1.0076 | 3.0 | 2283 | 0.6573 | 0.7346 | 0.7038 | 0.7153 | 0.7069 | |
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| 0.1369 | 4.0 | 3044 | 0.8941 | 0.7248 | 0.6855 | 0.6762 | 0.6796 | |
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| 0.0096 | 5.0 | 3805 | 1.1590 | 0.7264 | 0.6874 | 0.6911 | 0.6889 | |
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| 0.0728 | 6.0 | 4566 | 1.2896 | 0.7366 | 0.7001 | 0.6875 | 0.6910 | |
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| 0.0007 | 7.0 | 5327 | 1.5882 | 0.7297 | 0.7027 | 0.6787 | 0.6825 | |
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| 0.0106 | 8.0 | 6088 | 1.5117 | 0.7169 | 0.6803 | 0.6642 | 0.6693 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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