botdevringring/nl-naxai-ai-emotion-classification-101608122023(latest)
===============

The model is trained on the emotion classification task in **Dutch**. It uses 6 labels: ***anger***, ***fear***, ***joy*** ***love***, ***sadness***, ***surprise***.

This model is finetuned from [robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base).
[RobBERT](https://github.com/iPieter/RobBERT) is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task.

## Model Details
- Language: nl
- Problem type: Multi-class Classification
- Model Architecture: RobBERT
- Model Name: nl-naxai-ai-emotion-classification-101608122023
- Creation Date: 10:16h 08/12/2023

### Dataset Summary

The [botdevringring/NL_emotion_classification](https://huggingface.co/datasets/botdevringring/NL_emotion_classification) dataset is a balanced dataset translated from the English Twitter messages Emotion dataset of with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.


## Classification Report:

| **Label**    |  **Precision** | **Recall** | **f1-Score** | **Support** |
|-------------:|---------------:|-----------:|-------------:|------------:|
| anger   |  0.91 | 0.91 | 0.91 | 2956 |
| fear | 0.89 | 0.83 | 0.86 | 2956 |
| joy | 0.89 | 0.83 | 0.86 | 2956 |
| love | 0.88 | 0.91 | 0.89 | 2956 |
| sadness | 0.91 | 0.89 | 0.90 | 2956 |
| surprise | 0.87 | 0.97 | 0.92 | 2956 |


## 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/nl-naxai-ai-emotion-classification-101608122023',
    tokenizer='botdevringring/nl-naxai-ai-emotion-classification-101608122023'
)
result = analyzer(
    "ik blog omdat het iets is waar ik een passie voor heb."
)

result

[
  {
    'label': 'love',
    'score': 0.9728550314903259
  }
]
```
Or you can use cURL:

```
curl https://api-inference.huggingface.co/models/botdevringring/nl-naxai-ai-emotion-classification-101608122023 \
	-X POST \
	-d '{"inputs": "ik blog omdat het iets is waar ik een passie voor heb"}' \
	-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": "nl", "tags": ["text-classification", "RobBERT", "pytorch", "emotion", "latest"], "datasets": ["botdevringring/NL_emotion_classification", "dair-ai/emotion"], "metrics": ["Accuracy, F1 Score"], "widget": [{"text": "Ik weet niet zeker of ik me beter voel"}, {"text": "Ik denk dat het over het algemeen mijn vermogen heeft be\u00efnvloed om te vertrouwen op mijn vermogen om me niet gemarginaliseerd te voelen en ik sta zeer wantrouwend tegenover kliekjes en autoriteit"}, {"text": "Ik voel me de hele tijd raar tenzij ik lig"}, {"text": "ik blog omdat het iets is waar ik een passie voor heb"}], "pipeline_tag": "text-classification", "model-index": [{"name": "botdevringring/nl-naxai-ai-emotion-classification-101608122023", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.896, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.897, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.896, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.897, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.896, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.896, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.896, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.895, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.895, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.895, "name": "F1 Weighted", "verified": true}]}]}]}
transformers safetensors roberta text-classification RobBERT pytorch emotion latest nl dataset:botdevringring/NL_emotion_classification dataset:dair-ai/emotion model-index autotrain_compatible endpoints_compatible region:us