README.md

metadata

tags: autonlp
language: unk
widget:
  - text: I love AutoNLP 🤗
datasets:
  - Anamika/autonlp-data-Feedback1
co2_eq_emissions: 123.88023112815048

Model Trained Using AutoNLP

• Problem type: Multi-class Classification
• Model ID: 479512837
• CO2 Emissions (in grams): 123.88023112815048

Validation Metrics

• Loss: 0.6220805048942566
• Accuracy: 0.7961119332705503
• Macro F1: 0.7616345204219084
• Micro F1: 0.7961119332705503
• Weighted F1: 0.795387503907883
• Macro Precision: 0.782839455262034
• Micro Precision: 0.7961119332705503
• Weighted Precision: 0.7992606754484262
• Macro Recall: 0.7451485972167191
• Micro Recall: 0.7961119332705503
• Weighted Recall: 0.7961119332705503

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/Anamika/autonlp-Feedback1-479512837

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Anamika/autonlp-Feedback1-479512837", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Anamika/autonlp-Feedback1-479512837", use_auth_token=True)

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

outputs = model(**inputs)