
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)](https://huggingface.co/distilbert-base-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:

```python
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](https://www.linkedin.com/in/eduardobrigham/) for [Naxai](https://www.naxai.com/) powered by [The Ring Ring Company](https://www.ringring.be/)
{"language": "en", "tags": ["DistilBert", "Sentiment", "Pytorch"], "datasets": ["botdevringring/EN_Sentiment_3label_Dataset"], "metrics": ["Accuracy, F1 Score"], "widget": [{"text": "The delivery man was late and my package was badly damaged"}, {"text": "obviously, to sign the contract"}, {"text": "Good service for a fair price. Friendly and attentive staff."}, {"text": "Call directly to make an appointment during the week"}], "pipeline_tag": "text-classification", "model-index": [{"name": "botdevringring/en-naxai-ai-sentiment-classification-154012122023", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sentiment", "type": "sentiment", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.886, "name": "Accuracy", "verified": true}, {"type": "loss", "value": 0.337, "name": "loss", "verified": true}, {"type": "precision", "value": 0.827, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.886, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.88, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.777, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.886, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.886, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.794, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.886, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.881, "name": "F1 Weighted", "verified": true}, {"type": "samples per second", "value": 179.335, "name": "samples p/second", "verified": true}, {"type": "steps per second", "value": 22.432, "name": "steps p/second", "verified": true}, {"type": "epochs", "value": 7, "name": "epochs", "verified": true}]}]}]}
transformers tensorboard safetensors distilbert text-classification DistilBert Sentiment Pytorch en dataset:botdevringring/EN_Sentiment_3label_Dataset model-index autotrain_compatible endpoints_compatible has_space region:us