README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
widget:
  - text: I love AutoTrain
datasets:
  - IDQO/autotrain-data-liantis-profession-matcher-v08112023
co2_eq_emissions:
  emissions: 3.4066803387941684

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 100063147551
• CO2 Emissions (in grams): 3.4067

Validation Metrics

• Loss: 0.604
• Accuracy: 0.885
• Macro F1: 0.805
• Micro F1: 0.885
• Weighted F1: 0.871
• Macro Precision: 0.816
• Micro Precision: 0.885
• Weighted Precision: 0.868
• Macro Recall: 0.811
• Micro Recall: 0.885
• Weighted Recall: 0.885

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/IDQO/autotrain-liantis-profession-matcher-v08112023-100063147551

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("IDQO/autotrain-liantis-profession-matcher-v08112023-100063147551", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("IDQO/autotrain-liantis-profession-matcher-v08112023-100063147551", use_auth_token=True)

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

outputs = model(**inputs)