readme: ---
  tags: autotrain
  language: en
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - lewtun/autotrain-data-my-eval-project-615
  co2_eq_emissions: 172.04481351504182
  model-index:
  - name: bhadresh-savani/distilbert-base-uncased-emotion
    results:
    - task:
        name: Multi-class Classification
        type: text-classification
      dataset:
        type: emotion
        name: Emotion
        config: default
        split: test
      metrics:
         - name: Loss
           type: loss
           value: 0.17404702305793762
         - name: Accuracy
           type: accuracy
           value: 0.927
         - name: Macro F1
           type: macro_f1
           value: 0.8825061528287809
         - name: Recall
           type: micro_f1
           value: 0.927
         - name: Weighted F1
           type: weighted_f1
           value: 0.926876082854655
         - name: Macro Precision
           type: macro_precision
           value: 0.8880230732280744
         - name: Micro Precision
           type: micro_precision
           value: 0.927
         - name: Weighted Precision
           type: weighted_precision
           value: 0.9272902840835793
         - name: Macro Recall
           type: macro_recall
           value: 0.8790126653780703
         - name: Micro Recall
           type: micro_recall
           value: 0.927
         - name: Weighted Recall
           type: weighted_recall
           value: 0.927
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Binary Classification
  - Model ID: 5694363
  - CO2 Emissions (in grams): 172.04481351504182
  
  ## Validation Metrics
  
  - Loss: 0.2228243350982666
  - Accuracy: 0.9298
  - Precision: 0.9434585224927775
  - Recall: 0.9144
  - AUC: 0.9566112000000001
  - F1: 0.9287020109689214
  
  ## 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/lewtun/autotrain-my-eval-project-615-5694363
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```