readme: ---
  tags: autotrain
  language: en
  widget:
  - text: "I love AutoTrain 🤗"
  - Output: "Positive"
  datasets:
  - Souvikcmsa/autotrain-data-sentiment_analysis
  co2_eq_emissions: 0.029363397844935534
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification (3-class Sentiment Classification)
  
  ## Validation Metrics
  If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score. 
  - Loss: 0.4992932379245758
  - Accuracy: 0.799017824663514
  - Macro F1: 0.8021508522962549
  - Micro F1: 0.799017824663514
  - Weighted F1: 0.7993775463659935
  - Macro Precision: 0.80406197665167
  - Micro Precision: 0.799017824663514
  - Weighted Precision: 0.8000374433849405
  - Macro Recall: 0.8005261994732908
  - Micro Recall: 0.799017824663514
  - Weighted Recall: 0.799017824663514
  
  
  ## 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-762923428
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923428", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923428", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```
  OR
  ```
  from transformers import pipeline
  
  classifier = pipeline("text-classification", model = "Souvikcmsa/BERT_sentiment_analysis")
  classifier("I loved Star Wars so much!")# Positive
  classifier("A soccer game with multiple males playing. Some men are playing a sport.")# Neutral
  ```