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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: distilcamembert-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: 0.9714
- name: F1
type: f1
value: 0.9709909727152854
- name: Precision
type: precision
value: 0.9648256399919372
- name: Recall
type: recall
value: 0.9772356063699469
---
<!-- 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. -->
# distilcamembert-allocine
This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the allocine dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1066
- Accuracy: 0.9714
- F1: 0.9710
- Precision: 0.9648
- Recall: 0.9772
## 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 |
| :-----------: | :---: | :---: | :-------------: | :------: | :----: | :-------: | :----: |
| 0.1504 | 0.2 | 500 | 0.1290 | 0.9555 | 0.9542 | 0.9614 | 0.9470 |
| 0.1334 | 0.4 | 1000 | 0.1049 | 0.9624 | 0.9619 | 0.9536 | 0.9703 |
| 0.1158 | 0.6 | 1500 | 0.1052 | 0.963 | 0.9627 | 0.9498 | 0.9760 |
| 0.1153 | 0.8 | 2000 | 0.0949 | 0.9661 | 0.9653 | 0.9686 | 0.9620 |
| 0.1053 | 1.0 | 2500 | 0.0936 | 0.9666 | 0.9663 | 0.9542 | 0.9788 |
| 0.0755 | 1.2 | 3000 | 0.0987 | 0.97 | 0.9695 | 0.9644 | 0.9748 |
| 0.0716 | 1.4 | 3500 | 0.1078 | 0.9688 | 0.9684 | 0.9598 | 0.9772 |
| 0.0688 | 1.6 | 4000 | 0.1051 | 0.9673 | 0.9670 | 0.9552 | 0.9792 |
| 0.0691 | 1.8 | 4500 | 0.0940 | 0.9709 | 0.9704 | 0.9688 | 0.9720 |
| 0.0733 | 2.0 | 5000 | 0.1038 | 0.9686 | 0.9683 | 0.9558 | 0.9812 |
| 0.0476 | 2.2 | 5500 | 0.1066 | 0.9714 | 0.9710 | 0.9648 | 0.9772 |
| 0.047 | 2.4 | 6000 | 0.1098 | 0.9689 | 0.9686 | 0.9587 | 0.9788 |
| 0.0431 | 2.6 | 6500 | 0.1110 | 0.9711 | 0.9706 | 0.9666 | 0.9747 |
| 0.0464 | 2.8 | 7000 | 0.1149 | 0.9697 | 0.9694 | 0.9592 | 0.9798 |
| 0.0342 | 3.0 | 7500 | 0.1122 | 0.9703 | 0.9699 | 0.9621 | 0.9778 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
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