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---
language:
- fr
license: mit
tags:
- generated_from_trainer
datasets:
- allocine
widget:
- text: "Un film magnifique avec un duo d'acteurs excellent."
- text: "Grosse déception pour ce thriller qui peine à convaincre."
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: camembert-allocine
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: allocine
type: allocine
config: allocine
split: validation
args: allocine
metrics:
- name: Accuracy
type: accuracy
value: 0.97535
- name: F1
type: f1
value: 0.9749045558666326
- name: Precision
type: precision
value: 0.9722814498933902
- name: Recall
type: recall
value: 0.9775418538178848
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert-allocine
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the allocine dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0928
- Accuracy: 0.9754
- F1: 0.9749
- Precision: 0.9723
- Recall: 0.9775
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
| :-----------: | :---: | :---: | :-------------: | :------: | :----: | :-------: | :----: |
| 0.1276 | 0.2 | 500 | 0.1187 | 0.9623 | 0.9622 | 0.9462 | 0.9787 |
| 0.1013 | 0.4 | 1000 | 0.0917 | 0.9683 | 0.9675 | 0.9725 | 0.9625 |
| 0.1254 | 0.6 | 1500 | 0.0889 | 0.9701 | 0.9698 | 0.9597 | 0.9801 |
| 0.1004 | 0.8 | 2000 | 0.0792 | 0.9716 | 0.9709 | 0.9727 | 0.9691 |
| 0.1149 | 1.0 | 2500 | 0.0762 | 0.9727 | 0.9723 | 0.9673 | 0.9773 |
| 0.0574 | 1.2 | 3000 | 0.0849 | 0.9733 | 0.9729 | 0.9679 | 0.9780 |
| 0.0394 | 1.4 | 3500 | 0.1026 | 0.9718 | 0.9715 | 0.9595 | 0.9839 |
| 0.0401 | 1.6 | 4000 | 0.1065 | 0.9698 | 0.9697 | 0.9528 | 0.9872 |
| 0.0458 | 1.8 | 4500 | 0.0834 | 0.9744 | 0.9739 | 0.9715 | 0.9764 |
| 0.0554 | 2.0 | 5000 | 0.0873 | 0.9719 | 0.9717 | 0.9594 | 0.9844 |
| 0.0516 | 2.2 | 5500 | 0.0928 | 0.9754 | 0.9749 | 0.9723 | 0.9775 |
| 0.0355 | 2.4 | 6000 | 0.1017 | 0.9744 | 0.9741 | 0.9642 | 0.9842 |
| 0.0227 | 2.6 | 6500 | 0.0983 | 0.9748 | 0.9743 | 0.9729 | 0.9757 |
| 0.0359 | 2.8 | 7000 | 0.0990 | 0.9747 | 0.9743 | 0.9665 | 0.9823 |
| 0.0384 | 3.0 | 7500 | 0.1001 | 0.9746 | 0.9742 | 0.9662 | 0.9824 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
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