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BERT-Banking77 Model Trained Using AutoTrain

  • Problem type: Multi-class Classification
  • Model ID: 940131041
  • CO2 Emissions (in grams): 0.03330651014155927

Validation Metrics

  • Loss: 0.3505457043647766
  • Accuracy: 0.9263261296660118
  • Macro F1: 0.9268371013605569
  • Micro F1: 0.9263261296660118
  • Weighted F1: 0.9259954221865809
  • Macro Precision: 0.9305746406646502
  • Micro Precision: 0.9263261296660118
  • Weighted Precision: 0.929031563971418
  • Macro Recall: 0.9263724620088746
  • Micro Recall: 0.9263261296660118
  • Weighted Recall: 0.9263261296660118

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/philschmid/autotrain-does-it-work-940131041

Or Python API:

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/BERT-Banking77'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')
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Examples
This model can be loaded on the Inference API on-demand.

Dataset used to train philschmid/BERT-Banking77

Evaluation results