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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: distilcamembert-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: 0.9714 |
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- name: F1 |
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type: f1 |
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value: 0.9709909727152854 |
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- name: Precision |
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type: precision |
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value: 0.9648256399919372 |
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- name: Recall |
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type: recall |
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value: 0.9772356063699469 |
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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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# distilcamembert-allocine |
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This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the allocine dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1066 |
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- Accuracy: 0.9714 |
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- F1: 0.9710 |
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- Precision: 0.9648 |
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- Recall: 0.9772 |
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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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| 0.1504 | 0.2 | 500 | 0.1290 | 0.9555 | 0.9542 | 0.9614 | 0.9470 | |
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| 0.1334 | 0.4 | 1000 | 0.1049 | 0.9624 | 0.9619 | 0.9536 | 0.9703 | |
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| 0.1158 | 0.6 | 1500 | 0.1052 | 0.963 | 0.9627 | 0.9498 | 0.9760 | |
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| 0.1153 | 0.8 | 2000 | 0.0949 | 0.9661 | 0.9653 | 0.9686 | 0.9620 | |
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| 0.1053 | 1.0 | 2500 | 0.0936 | 0.9666 | 0.9663 | 0.9542 | 0.9788 | |
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| 0.0755 | 1.2 | 3000 | 0.0987 | 0.97 | 0.9695 | 0.9644 | 0.9748 | |
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| 0.0716 | 1.4 | 3500 | 0.1078 | 0.9688 | 0.9684 | 0.9598 | 0.9772 | |
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| 0.0688 | 1.6 | 4000 | 0.1051 | 0.9673 | 0.9670 | 0.9552 | 0.9792 | |
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| 0.0691 | 1.8 | 4500 | 0.0940 | 0.9709 | 0.9704 | 0.9688 | 0.9720 | |
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| 0.0733 | 2.0 | 5000 | 0.1038 | 0.9686 | 0.9683 | 0.9558 | 0.9812 | |
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| 0.0476 | 2.2 | 5500 | 0.1066 | 0.9714 | 0.9710 | 0.9648 | 0.9772 | |
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| 0.047 | 2.4 | 6000 | 0.1098 | 0.9689 | 0.9686 | 0.9587 | 0.9788 | |
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| 0.0431 | 2.6 | 6500 | 0.1110 | 0.9711 | 0.9706 | 0.9666 | 0.9747 | |
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| 0.0464 | 2.8 | 7000 | 0.1149 | 0.9697 | 0.9694 | 0.9592 | 0.9798 | |
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| 0.0342 | 3.0 | 7500 | 0.1122 | 0.9703 | 0.9699 | 0.9621 | 0.9778 | |
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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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