File size: 2,404 Bytes
2498348 35ca230 1d8bd0f 2498348 f5f91b4 2498348 3d63300 2498348 35ca230 2498348 00f0302 2498348 35ca230 2498348 35ca230 2498348 35ca230 2498348 35ca230 2498348 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
---
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)
``` |