
fr-naxai-ai-sentiment-classification-171830112023
===============

The model is trained on the sentiment classification task in the **French** language. It uses 3 labels: ***-1***, ***0*** and ***1***. These labels represent ***negative***, ***neutral*** and ***positive*** sentiments.

This model is finetuned from [Distilcamember](https://huggingface.co/cmarkea/distilcamembert-base).
A distillation version of the [CamemBERT](https://huggingface.co/camembert-base) model, a RoBERTa French model version.

## Model Details
- Language: fr
- Problem type: Multi-class Classification
- Model Architecture: distilcamembert
- Model Name: fr-naxai-ai-sentiment-classification-171830112023
- Creation date: 17:18h 30/11/2023

## Classification Report:

| **Label**    |  **Precision** | **Recall** | **f1-Score** | **Support** |
|-------------:|---------------:|-----------:|-------------:|------------:|
| -1 | 0.95 | 0.94 | 0.94 | 4961 |
| 0 | 0.64 | 0.62 | 0.63 | 891 |
| 1 | 0.91 | 0.94 | 0.92 | 2926 |
| | | | |  |
| accuracy | | | 0.90 | 8778 |
| macro avg | 0.83 | 0.83 | 0.83 | 8778 |
| weighted avg | 0.90 | 0.90 | 0.90 | 8778 |

## How to use this model
------------------------------------
You can use Python to access this model:

```python
from transformers import pipeline

analyzer = pipeline(
    task='text-classification',
    model=“botdevringring/fr-naxai-ai-sentiment-classification-171830112023”,
    tokenizer="botdevringring/fr-naxai-ai-sentiment-classification-171830112023"
)
result = analyzer(
    "J'aime me promener en forêt même si ça me donne mal aux pieds."
)

result

[
  {
    'label': '0',
    'score': 0.515792965888977
  }
]
```
Or you can use cURL:

```
curl https://api-inference.huggingface.co/models/botdevringring/fr-naxai-ai-sentiment-classification-171830112023 \
	-X POST \
	-d '{"inputs": "J'aime me promener en forêt même si ça me donne mal aux pieds."}' \
	-H 'Content-Type: application/json' \
	-H "Authorization: Bearer <Your HF API token>"
```

## Acknowledgements
Model trained by [Eduardo Brigham](https://www.linkedin.com/in/eduardobrigham/) for [Naxai](https://www.naxai.com/) powered by [The Ring Ring Company](https://www.ringring.be/)
{"language": "fr", "tags": ["Distilcamembert", "CamemBert", "Sentiment", "Pytorch"], "datasets": "botdevringring/FR_Sentiment_3label_Dataset", "metrics": ["Accuracy, F1 Score"], "widget": [{"text": "J'aime me promener en for\u00eat m\u00eame si \u00e7a me donne mal aux pieds."}, {"text": "Je pensais lire un livre nul, mais finalement je l'ai trouv\u00e9 super !"}, {"text": "Cette banque est tr\u00e8s bien, mais elle n'offre pas les services de paiements sans contact."}, {"text": "Cette banque est tr\u00e8s bien et elle offre en plus les services de paiements sans contact."}], "pipeline_tag": "text-classification", "model-index": [{"name": "botdevringring/fr-naxai-ai-emotion-classification-081808122023", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sentiment", "type": "sentiment", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.91, "name": "Accuracy", "verified": true}, {"type": "loss", "value": 0.337, "name": "loss", "verified": true}, {"type": "precision", "value": 0.907, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.91, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.907, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.828, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.91, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.91, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.84, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.91, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.908, "name": "F1 Weighted", "verified": true}]}]}]}
transformers safetensors camembert text-classification Distilcamembert CamemBert Sentiment Pytorch fr dataset:botdevringring/FR_Sentiment_3label_Dataset model-index autotrain_compatible endpoints_compatible region:us