
nl-naxai-ai-sentiment-classification-191229122023(latest)
===============

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

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-sentiment-classification-191229122023
- Creation date: 19:12h 29/12/2023

## Classification Report:

# Model metrics
- **Accuracy: 0.95**
- **Macro avg: 0.90**
- **Weighted avg: 0.95**
- **Support: 4676**

## Classification Report:

| **Label**    |  **Precision** | **Recall** | **f1-Score** | **Support** |
|-------------:|---------------:|-----------:|-------------:|------------:|
| -1 | 0.98 | 0.97 | 0.97 | 2797 |
| 0 | 0.82 | 0.72 | 0.77 | 427 |
| 1 | 0.93 | 0.98 | 0.96 | 1452 |
| accuracy | | | 0.95 | 4676 |
|  macro avg | 0.91 | 0.89 | 0.90 | 4676 |
| weighted avg | 0.95 | 0.95 | 0.95 | 4676 |


## 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-sentiment-classification-191229122023”,
    tokenizer="botdevringring/nl-naxai-ai-sentiment-classification-191229122023"
)
result = analyzer(
    "Deze bank is erg goed en biedt ook contactloze betaaldiensten."
)

result

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

```
curl https://api-inference.huggingface.co/models/botdevringring/nl-naxai-ai-sentiment-classification-191229122023 \
	-X POST \
	-d '{"inputs": "Deze bank is erg goed en biedt ook contactloze betaaldiensten."}' \
	-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": ["latest", "Pytorch", "RobBERT"], "datasets": "botdevringring/250k_NL_sentiment_3label", "widget": [{"text": "Ik hou van wandelen in het bos, zelfs als het pijn doet aan mijn voeten."}, {"text": "Ik dacht dat ik een waardeloos boek aan het lezen was, maar uiteindelijk vond ik het geweldig!"}, {"text": "Deze bank is erg goed, maar biedt geen contactloze betaaldiensten."}, {"text": "Deze bank is erg goed en biedt ook contactloze betaaldiensten."}], "pipeline_tag": "text-classification"}
transformers safetensors roberta text-classification latest Pytorch RobBERT nl dataset:botdevringring/250k_NL_sentiment_3label autotrain_compatible endpoints_compatible region:us