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# 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

{"language": ["fr"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["allocine"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Un film magnifique avec un duo d'acteurs excellent."}, {"text": "Grosse d\u00e9ception pour ce thriller qui peine \u00e0 convaincre."}], "model-index": [{"name": "camembert-allocine", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "allocine", "type": "allocine", "config": "allocine", "split": "validation", "args": "allocine"}, "metrics": [{"type": "accuracy", "value": 0.97535, "name": "Accuracy"}, {"type": "f1", "value": 0.9749045558666326, "name": "F1"}, {"type": "precision", "value": 0.9722814498933902, "name": "Precision"}, {"type": "recall", "value": 0.9775418538178848, "name": "Recall"}]}]}]}
transformers pytorch tf tensorboard camembert text-classification generated_from_trainer fr dataset:allocine license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #tensorboard #camembert #text-classification #generated_from_trainer #fr #dataset-allocine #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

camembert-allocine
==================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tf #tensorboard #camembert #text-classification #generated_from_trainer #fr #dataset-allocine #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tf #tensorboard #camembert #text-classification #generated_from_trainer #fr #dataset-allocine #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2