readme: ---
  tags: autotrain
  language: en
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - Souvikcmsa/autotrain-data-sentiment_analysis
  co2_eq_emissions: 0.015536746909294205
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 762923432
  - CO2 Emissions (in grams): 0.015536746909294205
  
  ## Validation Metrics
  
  - Loss: 0.49825894832611084
  - Accuracy: 0.7962895598399418
  - Macro F1: 0.7997458031044901
  - Micro F1: 0.7962895598399418
  - Weighted F1: 0.796365325858282
  - Macro Precision: 0.7995724418486833
  - Micro Precision: 0.7962895598399418
  - Weighted Precision: 0.7965384250324863
  - Macro Recall: 0.8000290112564951
  - Micro Recall: 0.7962895598399418
  - Weighted Recall: 0.7962895598399418
  
  
  ## 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-sentiment_analysis-762923432
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923432", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923432", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```