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--- |
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language: |
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- fr |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- allocine |
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widget: |
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- text: "Un film magnifique avec un duo d'acteurs excellent." |
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- text: "Grosse déception pour ce thriller qui peine à convaincre." |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: camembert-allocine |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: allocine |
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type: allocine |
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config: allocine |
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split: validation |
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args: allocine |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.97535 |
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- name: F1 |
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type: f1 |
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value: 0.9749045558666326 |
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- name: Precision |
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type: precision |
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value: 0.9722814498933902 |
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- name: Recall |
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type: recall |
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value: 0.9775418538178848 |
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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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# camembert-allocine |
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the allocine dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0928 |
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- Accuracy: 0.9754 |
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- F1: 0.9749 |
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- Precision: 0.9723 |
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- Recall: 0.9775 |
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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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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| :-----------: | :---: | :---: | :-------------: | :------: | :----: | :-------: | :----: | |
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| 0.1276 | 0.2 | 500 | 0.1187 | 0.9623 | 0.9622 | 0.9462 | 0.9787 | |
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| 0.1013 | 0.4 | 1000 | 0.0917 | 0.9683 | 0.9675 | 0.9725 | 0.9625 | |
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| 0.1254 | 0.6 | 1500 | 0.0889 | 0.9701 | 0.9698 | 0.9597 | 0.9801 | |
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| 0.1004 | 0.8 | 2000 | 0.0792 | 0.9716 | 0.9709 | 0.9727 | 0.9691 | |
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| 0.1149 | 1.0 | 2500 | 0.0762 | 0.9727 | 0.9723 | 0.9673 | 0.9773 | |
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| 0.0574 | 1.2 | 3000 | 0.0849 | 0.9733 | 0.9729 | 0.9679 | 0.9780 | |
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| 0.0394 | 1.4 | 3500 | 0.1026 | 0.9718 | 0.9715 | 0.9595 | 0.9839 | |
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| 0.0401 | 1.6 | 4000 | 0.1065 | 0.9698 | 0.9697 | 0.9528 | 0.9872 | |
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| 0.0458 | 1.8 | 4500 | 0.0834 | 0.9744 | 0.9739 | 0.9715 | 0.9764 | |
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| 0.0554 | 2.0 | 5000 | 0.0873 | 0.9719 | 0.9717 | 0.9594 | 0.9844 | |
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| 0.0516 | 2.2 | 5500 | 0.0928 | 0.9754 | 0.9749 | 0.9723 | 0.9775 | |
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| 0.0355 | 2.4 | 6000 | 0.1017 | 0.9744 | 0.9741 | 0.9642 | 0.9842 | |
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| 0.0227 | 2.6 | 6500 | 0.0983 | 0.9748 | 0.9743 | 0.9729 | 0.9757 | |
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| 0.0359 | 2.8 | 7000 | 0.0990 | 0.9747 | 0.9743 | 0.9665 | 0.9823 | |
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| 0.0384 | 3.0 | 7500 | 0.1001 | 0.9746 | 0.9742 | 0.9662 | 0.9824 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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