en-naxai-ai-sentiment-classification-154012122023(latest)

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

This model is finetuned from DistilBERT base model (uncased). This model is a distilled version of the BERT base model. This model is uncased: it does not make a difference between english and English.

Model Details

• Language: en
• Problem type: Multi-class Classification
• Model Architecture: Distilbert base uncased
• Model Name: en-naxai-ai-sentiment-classification-154012122023
• Creation date: 15:40h 12/12/2023
• CO2 Emissions (in grams): 0.06

Classification Report:

Label	Precision	Recall	f1-Score	Support
-1	0.94	0.94	0.94	4870
0	0.58	0.59	0.59	867
1	0.91	0.90	0.91	2856

How to use this model

You can use Python to access this model:

from transformers import pipeline

analyzer = pipeline(
    task='text-classification',
    model=“botdevringring/en-naxai-ai-sentiment-classification-154012122023”,
    tokenizer="botdevringring/en-naxai-ai-sentiment-classification-154012122023"
)
result = analyzer(
    "Good service for a fair price. Friendly and attentive staff."
)

result

[{'label': '2', 'score': 0.515792965888977}]

Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("botdevringring/en-naxai-ai-sentiment-classification-154012122023", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("botdevringring/en-naxai-ai-sentiment-classification-154012122023", use_auth_token=True)

inputs = tokenizer("Good service for a fair price. Friendly and attentive staff.", return_tensors="pt")

outputs = model(**inputs)

Or you can use cURL:

curl https://api-inference.huggingface.co/models/botdevringring/en-naxai-ai-sentiment-classification-154012122023 \
    -X POST \
    -d '{"inputs": "Good service for a fair price. Friendly and attentive staff."}' \
    -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