README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - de
widget:
  - text: I love AutoTrain
datasets:
  - happycart/autotrain-data-product-string-to-ingredient-id-31-10-23
co2_eq_emissions:
  emissions: 23.774870133879485

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 98528147114
• CO2 Emissions (in grams): 23.7749

Validation Metrics

• Loss: 1.385
• Accuracy: 0.689
• Macro F1: 0.564
• Micro F1: 0.689
• Weighted F1: 0.657
• Macro Precision: 0.557
• Micro Precision: 0.689
• Weighted Precision: 0.656
• Macro Recall: 0.606
• Micro Recall: 0.689
• Weighted Recall: 0.689

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/happycart/autotrain-product-string-to-ingredient-id-31-10-23-98528147114

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("happycart/autotrain-product-string-to-ingredient-id-31-10-23-98528147114", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("happycart/autotrain-product-string-to-ingredient-id-31-10-23-98528147114", use_auth_token=True)

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

outputs = model(**inputs)