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--- |
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base_model: FPTAI/vibert-base-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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- f1 |
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model-index: |
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- name: vibert-base-cased-ed |
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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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# vibert-base-cased-ed |
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This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0595 |
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- F1 Micro: 0.7034 |
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- F1 Macro: 0.0430 |
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- Accuracy: 0.6374 |
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- Recall Micro: 0.6094 |
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- Precision Micro: 0.8317 |
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- Recall Macro: 0.0392 |
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- Precision Macro: 0.0621 |
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- F1: 0.5913 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Recall Micro | Precision Micro | Recall Macro | Precision Macro | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:------------:|:---------------:|:------------:|:---------------:|:------:| |
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| 0.0696 | 1.0 | 1526 | 0.0711 | 0.6892 | 0.0243 | 0.7054 | 0.6737 | 0.7054 | 0.0294 | 0.0207 | 0.5573 | |
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| 0.055 | 2.0 | 3052 | 0.0622 | 0.6965 | 0.0252 | 0.6345 | 0.6060 | 0.8187 | 0.0265 | 0.0241 | 0.5775 | |
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| 0.0631 | 3.0 | 4578 | 0.0598 | 0.7054 | 0.0255 | 0.6436 | 0.6147 | 0.8274 | 0.0268 | 0.0243 | 0.5847 | |
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| 0.0534 | 4.0 | 6104 | 0.0591 | 0.6980 | 0.0260 | 0.6268 | 0.5989 | 0.8362 | 0.0265 | 0.0540 | 0.5809 | |
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| 0.0296 | 5.0 | 7630 | 0.0595 | 0.7034 | 0.0430 | 0.6374 | 0.6094 | 0.8317 | 0.0392 | 0.0621 | 0.5913 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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