readme: ---
  tags: autotrain
  language: en
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - Souvikcmsa/autotrain-data-sentimentAnalysis_By_Souvik
  co2_eq_emissions: 4.453029772491864
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 762623422
  - CO2 Emissions (in grams): 4.453029772491864
  
  ## Validation Metrics
  
  - Loss: 0.40843138098716736
  - Accuracy: 0.8302828618968386
  - Macro F1: 0.8302447939743022
  - Micro F1: 0.8302828618968385
  - Weighted F1: 0.8302151855901072
  - Macro Precision: 0.8310980209442669
  - Micro Precision: 0.8302828618968386
  - Weighted Precision: 0.8313262654775467
  - Macro Recall: 0.8305699539252172
  - Micro Recall: 0.8302828618968386
  - Weighted Recall: 0.8302828618968386
  
  
  ## 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/Souvikcmsa/autotrain-sentimentAnalysis_By_Souvik-762623422
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/autotrain-sentimentAnalysis_By_Souvik-762623422", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/autotrain-sentimentAnalysis_By_Souvik-762623422", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```