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crcb
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isear_bert

Text Classification
Transformers
PyTorch
Enawené-Nawé
roberta
Trained with AutoTrain
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isear_bert / README.md
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metadata
tags: autotrain
language: unk
widget:
  - text: I love AutoTrain 🤗
datasets:
  - crcb/autotrain-data-isear_bert
co2_eq_emissions: 0.026027055434994496

Model Trained Using AutoTrain

  • Problem type: Multi-class Classification
  • Model ID: 786224257
  • CO2 Emissions (in grams): 0.026027055434994496

Validation Metrics

  • Loss: 0.8348872065544128
  • Accuracy: 0.7272727272727273
  • Macro F1: 0.7230931630686932
  • Micro F1: 0.7272727272727273
  • Weighted F1: 0.7236599456423468
  • Macro Precision: 0.7328252157220334
  • Micro Precision: 0.7272727272727273
  • Weighted Precision: 0.7336599708829821
  • Macro Recall: 0.7270448163292604
  • Micro Recall: 0.7272727272727273
  • Weighted Recall: 0.7272727272727273

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/crcb/autotrain-isear_bert-786224257

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-isear_bert-786224257", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-isear_bert-786224257", use_auth_token=True)

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

outputs = model(**inputs)