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--- |
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tags: |
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- text-classification |
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language: |
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- en |
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widget: |
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- text: I don't feel like you trust me to do my job. |
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example_title: "Negative Example 1" |
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- text: "This service was honestly one of the best I've experienced, I'll definitely come back!" |
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example_title: "Positive Example 1" |
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- 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." |
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example_title: "Negative Example 2" |
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- 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." |
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example_title: "Positive Example 2" |
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datasets: |
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- Kaludi/data-reviews-sentiment-analysis |
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co2_eq_emissions: |
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emissions: 24.76716845191504 |
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--- |
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# Reviews Sentiment Analysis |
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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. |
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## Training Procedure |
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- learning_rate = 1e-5 |
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- batch_size = 32 |
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- warmup = 600 |
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- max_seq_length = 128 |
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- num_train_epochs = 10.0 |
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## Validation Metrics |
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- Loss: 0.159 |
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- Accuracy: 0.952 |
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- Precision: 0.965 |
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- Recall: 0.938 |
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- AUC: 0.988 |
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- F1: 0.951 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True) |
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inputs = tokenizer("I don't feel like you trust me to do my job.", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
