metadata

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/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)