readme: ---
  tags: autonlp
  language: en
  widget:
  - text: "I am still waiting on my card?"
  datasets:
  - banking77
  model-index:
  - name: RoBERTa-Banking77
    results:
    - task: 
        name: Text Classification
        type: text-classification
      dataset:
        name: "BANKING77" 
        type: banking77
      metrics:
         - name: Accuracy
           type: accuracy
           value: 93.51
         - name: Macro F1
           type: macro-f1
           value: 93.49
         - name: Weighted F1
           type: weighted-f1
           value: 93.49
  ---
  # `RoBERTa-Banking77` trained using autoNLP
  
  
  - Problem type: Multi-class Classification
  
  ## Validation Metrics
  
  - Loss: 0.27382662892341614
  - Accuracy: 0.935064935064935
  - Macro F1: 0.934939412967268
  - Micro F1: 0.935064935064935
  - Weighted F1: 0.934939412967268
  - Macro Precision: 0.9372295644352715
  - Micro Precision: 0.935064935064935
  - Weighted Precision: 0.9372295644352717
  - Macro Recall: 0.9350649350649349
  - Micro Recall: 0.935064935064935
  - Weighted Recall: 0.935064935064935
  
  
  ## 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 AutoNLP"}' https://api-inference.huggingface.co/models/philschmid/RoBERTa-Banking77
  ```
  
  Or Python API:
  
  ```py
  from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
  
  model_id = 'philschmid/RoBERTa-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?')
  ```