readme: ---
  tags:
  - text-classification
  language:
  - en
  widget:
  - text: I don't feel like you trust me to do my job.
    example_title: "Negative Example 1"
  - text: "This service was honestly one of the best I've experienced, I'll definitely come back!"
    example_title: "Positive Example 1"
  - text: "I was extremely disappointed with this product. The quality was terrible and it broke after only a few days of use. Customer service was unhelpful and unresponsive. I would not recommend this product to anyone."
    example_title: "Negative Example 2"
  - text: "I am so impressed with this product! The quality is outstanding and it has exceeded all of my expectations. The customer service team was also incredibly helpful and responsive to any questions I had. I highly recommend this product to anyone in need of a top-notch, reliable solution."
    example_title: "Positive Example 2"
  datasets:
  - Kaludi/autotrain-data-reviews-sentiment-analysis
  co2_eq_emissions:
    emissions: 24.76716845191504
  ---
  
  # Reviews Sentiment Analysis
  
  A tool that analyzes the overall sentiment of customer reviews for a specific product or service, whether it’s positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model ‘Reviews-Sentiment-Analysis’ trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.
  
  ## Training Procedure
  
  - learning_rate = 1e-5
  - batch_size = 32
  - warmup = 600
  - max_seq_length = 128
  - num_train_epochs = 10.0
  
  ## Validation Metrics
  
  - Loss: 0.159
  - Accuracy: 0.952
  - Precision: 0.965
  - Recall: 0.938
  - AUC: 0.988
  - F1: 0.951
  
  ## 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 don't feel like you trust me to do my job."}' https://api-inference.huggingface.co/models/Kaludi/Reviews-Sentiment-Analysis
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True)
  
  inputs = tokenizer("I don't feel like you trust me to do my job.", return_tensors="pt")
  
  outputs = model(**inputs)
  ```