README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
widget:
  - text: I love AutoTrain
datasets:
  - yeye776/autotrain-data-intent-classification-6categories-distilbert
co2_eq_emissions:
  emissions: 0.4747647804932346

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 89087143849
• CO2 Emissions (in grams): 0.4748

Validation Metrics

• Loss: 0.542
• Accuracy: 0.881
• Macro F1: 0.833
• Micro F1: 0.881
• Weighted F1: 0.854
• Macro Precision: 0.918
• Micro Precision: 0.881
• Weighted Precision: 0.901
• Macro Recall: 0.846
• Micro Recall: 0.881
• Weighted Recall: 0.881

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/yeye776/autotrain-intent-classification-6categories-distilbert-89087143849

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("yeye776/autotrain-intent-classification-6categories-distilbert-89087143849", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("yeye776/autotrain-intent-classification-6categories-distilbert-89087143849", use_auth_token=True)

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

outputs = model(**inputs)