# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Kaludi/Reviews-Sentiment-Analysis")
model = AutoModelForSequenceClassification.from_pretrained("Kaludi/Reviews-Sentiment-Analysis")Quick Links
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)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kaludi/Reviews-Sentiment-Analysis")