README.md

metadata

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
1 - Metrics and Targets
2 - Strategy
3 - Risk Management
4 - Governance

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/autotrain-climate-tcfd-recommendation

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

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

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

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

outputs = model(**inputs)