README.md

metadata

tags: autonlp
language: en
widget:
  - text: I love AutoNLP 🤗
datasets:
  - ds198799/autonlp-data-predict_ROI_1
co2_eq_emissions: 2.7516207978192737

Model Trained Using AutoNLP

• Problem type: Multi-class Classification
• Model ID: 29797722
• CO2 Emissions (in grams): 2.7516207978192737

Validation Metrics

• Loss: 0.6113826036453247
• Accuracy: 0.7559139784946236
• Macro F1: 0.4594734612976928
• Micro F1: 0.7559139784946236
• Weighted F1: 0.7195080232106192
• Macro Precision: 0.7175166413412577
• Micro Precision: 0.7559139784946236
• Weighted Precision: 0.7383048259333735
• Macro Recall: 0.4482203645846237
• Micro Recall: 0.7559139784946236
• Weighted Recall: 0.7559139784946236

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 AutoNLP"}' https://api-inference.huggingface.co/models/ds198799/autonlp-predict_ROI_1-29797722

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ds198799/autonlp-predict_ROI_1-29797722", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ds198799/autonlp-predict_ROI_1-29797722", use_auth_token=True)

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

outputs = model(**inputs)