README.md

---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ramnika003/autotrain-data-sentiment_analysis_project
co2_eq_emissions: 10.03748863138583
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583

## Validation Metrics

- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148
- Macro Precision: 0.7658234531650739
- Micro Precision: 0.768964665184087
- Weighted Precision: 0.7684017544026074
- Macro Recall: 0.7603505092881394
- Micro Recall: 0.768964665184087
- Weighted Recall: 0.768964665184087


## 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/ramnika003/autotrain-sentiment_analysis_project-705021428
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)

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

outputs = model(**inputs)
```