README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - unk
widget:
  - text: I love AutoTrain 🤗
datasets:
  - AyoubChLin/autotrain-data-roberta-large-bbc_news
co2_eq_emissions:
  emissions: 1.9843929651071104

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 48943118458
• CO2 Emissions (in grams): 1.9844

Validation Metrics

• Loss: 0.062
• Accuracy: 0.991
• Macro F1: 0.991
• Micro F1: 0.991
• Weighted F1: 0.991
• Macro Precision: 0.991
• Micro Precision: 0.991
• Weighted Precision: 0.991
• Macro Recall: 0.992
• Micro Recall: 0.991
• Weighted Recall: 0.991

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/AyoubChLin/autotrain-roberta-large-bbc_news-48943118458

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/autotrain-roberta-large-bbc_news-48943118458", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/autotrain-roberta-large-bbc_news-48943118458", use_auth_token=True)

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

outputs = model(**inputs)