README.md

metadata

tags:
  - text-classification
language:
  - it
widget:
  - text: >-
      L utente concede una licenza non esclusiva, trasferibile, sublicenziabile,
      non soggetta a royalty e valida in tutto il mondo.
co2_eq_emissions:
  emissions: 0.022138627441573373

Model Trained

• Problem type: Multi-class Classification
• Model ID: pe-llm-0.1
• CO2 Emissions (in grams): 0.0221

Validation Metrics

• Loss: 0.841
• Accuracy: 0.761
• Macro F1: 0.644
• Micro F1: 0.761
• Weighted F1: 0.750
• Macro Precision: 0.679
• Micro Precision: 0.761
• Weighted Precision: 0.748
• Macro Recall: 0.635
• Micro Recall: 0.761
• Weighted Recall: 0.761

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/kolkata97/autotrain-pe-llm-0.6-89942144050

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("kolkata97/autotrain-pe-llm-0.6-89942144050", use_auth_token=True)

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

outputs = model(**inputs)