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. 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:

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 for Naxai powered by The Ring Ring Company