mixed negative no_impact positive
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- charly/autotrain-data-sentiment-4
co2_eq_emissions: 0.007597570744740809
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 812425472
- CO2 Emissions (in grams): 0.007597570744740809

## Validation Metrics

- Loss: 0.5105093121528625
- Accuracy: 0.8268156424581006
- Macro F1: 0.6020923520923521
- Micro F1: 0.8268156424581006
- Weighted F1: 0.8021395116367184
- Macro Precision: 0.5907986111111111
- Micro Precision: 0.8268156424581006
- Weighted Precision: 0.7792248603351954
- Macro Recall: 0.6141625496464206
- Micro Recall: 0.8268156424581006
- Weighted Recall: 0.8268156424581006


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/charly/autotrain-sentiment-4-812425472
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("charly/autotrain-sentiment-4-812425472", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("charly/autotrain-sentiment-4-812425472", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```