README.md

metadata

tags: autotrain
language: unk
widget:
  - text: الكل ينتقد الرئيس على إخفاقاته
datasets:
  - cjbarrie/autotrain-data-masress-medcrit-binary-5
co2_eq_emissions: 0.01017487638098474

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 937130980
• CO2 Emissions (in grams): 0.01017487638098474

Validation Metrics

• Loss: 0.757265031337738
• Accuracy: 0.7551020408163265
• Macro F1: 0.7202470830473576
• Micro F1: 0.7551020408163265
• Weighted F1: 0.7594301962377263
• Macro Precision: 0.718716577540107
• Micro Precision: 0.7551020408163265
• Weighted Precision: 0.7711448215649895
• Macro Recall: 0.7285714285714286
• Micro Recall: 0.7551020408163265
• Weighted Recall: 0.7551020408163265

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/cjbarrie/autotrain-masress-medcrit-binary-5-937130980

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("cjbarrie/autotrain-masress-medcrit-binary-5-937130980", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("cjbarrie/autotrain-masress-medcrit-binary-5-937130980", use_auth_token=True)

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

outputs = model(**inputs)