README.md

---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Sachinkelenjaguri/autotrain-data-sachin-test-summarizer
co2_eq_emissions:
  emissions: 0.001210370183555198


---
# Class:
  0- Risk <br>
  1-Neutral <br>
  2-Oppertunity <br> 

  
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 55107128708
- CO2 Emissions (in grams): 0.0012

## Validation Metrics

- Loss: 0.516
- Accuracy: 0.806
- Macro F1: 0.783
- Micro F1: 0.806
- Weighted F1: 0.806
- Macro Precision: 0.777
- Micro Precision: 0.806
- Weighted Precision: 0.809
- Macro Recall: 0.793
- Micro Recall: 0.806
- Weighted Recall: 0.806


## 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/Sachinkelenjaguri/Sachinkelenjaguri/climate_sentiment_classifier
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Sachinkelenjaguri/climate_sentiment_classifier", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Sachinkelenjaguri/climate_sentiment_classifier", use_auth_token=True)

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

outputs = model(**inputs)
```