README.md

metadata

tags: autotrain
language: en
widget:
  - text: I am still waiting on my card?
datasets:
  - banking77
model-index:
  - name: BERT-Banking77
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: BANKING77
          type: banking77
        metrics:
          - name: Accuracy
            type: accuracy
            value: 91.99
          - name: Macro F1
            type: macro-f1
            value: 91.99
          - name: Weighted F1
            type: weighted-f1
            value: 91.99
co2_eq_emissions: 5.632805352029529

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 940131045
• CO2 Emissions (in grams): 5.632805352029529

Validation Metrics

• Loss: 0.3392622470855713
• Accuracy: 0.9199410609037328
• Macro F1: 0.9199390885956755
• Micro F1: 0.9199410609037327
• Weighted F1: 0.9198140295005729
• Macro Precision: 0.9235531521509113
• Micro Precision: 0.9199410609037328
• Weighted Precision: 0.9228777883152248
• Macro Recall: 0.919570805773292
• Micro Recall: 0.9199410609037328
• Weighted Recall: 0.9199410609037328

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-940131045

Or Python API:

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/DistilBERT-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?')