README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
widget:
  - text: I love AutoTrain 🤗
datasets:
  - derek-thomas/autotrain-data-banking77-distilroberta
co2_eq_emissions:
  emissions: 2.4242359759818415

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 44209111546
• CO2 Emissions (in grams): 2.4242

Validation Metrics

• Loss: 0.305
• Accuracy: 0.927
• Macro F1: 0.928
• Micro F1: 0.927
• Weighted F1: 0.927
• Macro Precision: 0.935
• Micro Precision: 0.927
• Weighted Precision: 0.932
• Macro Recall: 0.925
• Micro Recall: 0.927
• Weighted Recall: 0.927

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/derek-thomas/autotrain-banking77-distilroberta-44209111546

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("derek-thomas/autotrain-banking77-distilroberta-44209111546", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("derek-thomas/autotrain-banking77-distilroberta-44209111546", use_auth_token=True)

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

outputs = model(**inputs)