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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


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)
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Dataset used to train Kaludi/Reviews-Sentiment-Analysis

Space using Kaludi/Reviews-Sentiment-Analysis 1