README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
widget:
  - text: I love AutoTrain 🤗
datasets:
  - MMars/autotrain-data-arabertv2_flodusta
co2_eq_emissions:
  emissions: 3.6263155149619304

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 2782582128
• CO2 Emissions (in grams): 3.6263

Validation Metrics

• Loss: 0.144
• Accuracy: 0.953
• Macro F1: 0.951
• Micro F1: 0.953
• Weighted F1: 0.953
• Macro Precision: 0.951
• Micro Precision: 0.953
• Weighted Precision: 0.953
• Macro Recall: 0.952
• Micro Recall: 0.953
• Weighted Recall: 0.953

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/MMars/autotrain-arabertv2_flodusta-2782582128

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MMars/autotrain-arabertv2_flodusta-2782582128", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MMars/autotrain-arabertv2_flodusta-2782582128", use_auth_token=True)

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

outputs = model(**inputs)