README.md

---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Sachinkelenjaguri/autotrain-data-climate-tcfd-recommendation
co2_eq_emissions:
  emissions: 0.0015416078395342335
---
# Class
0 - None <br>
1 - Metrics and Targets  <br>
2 - Strategy  <br>
3 - Risk Management  <br>
4 - Governance  <br>
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 55122128742
- CO2 Emissions (in grams): 0.0015

## Validation Metrics

- Loss: 0.646
- Accuracy: 0.777
- Macro F1: 0.727
- Micro F1: 0.777
- Weighted F1: 0.779
- Macro Precision: 0.734
- Micro Precision: 0.777
- Weighted Precision: 0.786
- Macro Recall: 0.731
- Micro Recall: 0.777
- Weighted Recall: 0.777


## 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/climate-tcfd-recommendation
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Sachinkelenjaguri/climate-tcfd-recommendation", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Sachinkelenjaguri/climate-tcfd-recommendation", use_auth_token=True)

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

outputs = model(**inputs)
```