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miguelonana
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camembert-bank-post-classifier

Text Classification
Transformers
Safetensors
French
camembert
Generated from Trainer
french
banking
classification
Model card Files
xet
Community
miguelonana commited on
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End of training

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  1. README.md +12 -9
  2. model.safetensors +1 -1
README.md CHANGED
@@ -19,9 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6653
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- - Accuracy: 0.8661
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- - F1: 0.8633
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  ## Model description
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@@ -46,17 +46,20 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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 | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 1.6198 | 1.0 | 124 | 1.3080 | 0.6917 | 0.6261 |
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- | 1.1239 | 2.0 | 248 | 0.9667 | 0.8114 | 0.8046 |
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- | 0.7734 | 3.0 | 372 | 0.7734 | 0.8560 | 0.8513 |
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- | 0.6366 | 4.0 | 496 | 0.6825 | 0.8621 | 0.8559 |
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- | 0.5301 | 5.0 | 620 | 0.6653 | 0.8661 | 0.8633 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5783
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+ - Accuracy: 0.8682
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+ - F1: 0.8663
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 8
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 1.557 | 1.0 | 124 | 1.2381 | 0.7181 | 0.6649 |
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+ | 1.0415 | 2.0 | 248 | 0.8830 | 0.8316 | 0.8254 |
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+ | 0.6258 | 3.0 | 372 | 0.6725 | 0.8560 | 0.8523 |
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+ | 0.4723 | 4.0 | 496 | 0.6103 | 0.8540 | 0.8516 |
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+ | 0.3092 | 5.0 | 620 | 0.5921 | 0.8600 | 0.8583 |
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+ | 0.2244 | 6.0 | 744 | 0.5722 | 0.8641 | 0.8624 |
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+ | 0.1482 | 7.0 | 868 | 0.5719 | 0.8641 | 0.8626 |
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+ | 0.1493 | 8.0 | 992 | 0.5783 | 0.8682 | 0.8663 |
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  ### Framework versions
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