README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
widget:
  - text: I love AutoTrain
datasets:
  - Vishwas1/autotrain-data-customer-intent-bert
co2_eq_emissions:
  emissions: 0.006826171695324974

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 99967147525
• CO2 Emissions (in grams): 0.0068

Validation Metrics

• Loss: 0.013
• Accuracy: 0.998
• Macro F1: 0.999
• Micro F1: 0.998
• Weighted F1: 0.998
• Macro Precision: 0.999
• Micro Precision: 0.998
• Weighted Precision: 0.998
• Macro Recall: 0.999
• Micro Recall: 0.998
• Weighted Recall: 0.998

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/Vishwas1/autotrain-customer-intent-bert-99967147525

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Vishwas1/autotrain-customer-intent-bert-99967147525", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Vishwas1/autotrain-customer-intent-bert-99967147525", use_auth_token=True)

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

outputs = model(**inputs)