readme: ---
  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
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: banking77
        type: banking77
        config: default
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.922077922077922
        verified: true
      - name: Precision Macro
        type: precision
        value: 0.9256326708783564
        verified: true
      - name: Precision Micro
        type: precision
        value: 0.922077922077922
        verified: true
      - name: Precision Weighted
        type: precision
        value: 0.9256326708783565
        verified: true
      - name: Recall Macro
        type: recall
        value: 0.922077922077922
        verified: true
      - name: Recall Micro
        type: recall
        value: 0.922077922077922
        verified: true
      - name: Recall Weighted
        type: recall
        value: 0.922077922077922
        verified: true
      - name: F1 Macro
        type: f1
        value: 0.9221617304411865
        verified: true
      - name: F1 Micro
        type: f1
        value: 0.922077922077922
        verified: true
      - name: F1 Weighted
        type: f1
        value: 0.9221617304411867
        verified: true
      - name: loss
        type: loss
        value: 0.31692808866500854
        verified: true
  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?')
  ```