readme: ---
  tags: autotrain
  language: unk
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - ramnika003/autotrain-data-sentiment_analysis_project
  co2_eq_emissions: 10.03748863138583
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 705021428
  - CO2 Emissions (in grams): 10.03748863138583
  
  ## Validation Metrics
  
  - Loss: 0.5534441471099854
  - Accuracy: 0.768964665184087
  - Macro F1: 0.7629008163259284
  - Micro F1: 0.768964665184087
  - Weighted F1: 0.7685397042536148
  - Macro Precision: 0.7658234531650739
  - Micro Precision: 0.768964665184087
  - Weighted Precision: 0.7684017544026074
  - Macro Recall: 0.7603505092881394
  - Micro Recall: 0.768964665184087
  - Weighted Recall: 0.768964665184087
  
  
  ## 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/ramnika003/autotrain-sentiment_analysis_project-705021428
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```