Update README.md

README.md

@@ -1,11 +1,10 @@
 ---
 tags:
-- autotrain
 - text-classification
 language:
 - en
 widget:
-- text: "I don't feel like you trust me to do my job."
+- text: I don't feel like you trust me to do my job.
 datasets:
 - Kaludi/autotrain-data-reviews-sentiment-analysis
 co2_eq_emissions:
@@ -14,9 +13,7 @@ co2_eq_emissions:
 
 # Reviews Sentiment Analysis
 
-- Problem type: Binary Classification
-- Model ID: 3125888400
-- CO2 Emissions (in grams): 24.7672
+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.
 
 ## Validation Metrics
 
@@ -32,7 +29,7 @@ co2_eq_emissions:
 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 love AutoTrain"}' https://api-inference.huggingface.co/models/Kaludi/autotrain-reviews-sentiment-analysis-3125888400
+$ 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:
@@ -40,11 +37,11 @@ Or Python API:
 ```
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-model = AutoModelForSequenceClassification.from_pretrained("Kaludi/autotrain-reviews-sentiment-analysis-3125888400", use_auth_token=True)
+model = AutoModelForSequenceClassification.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True)
 
-tokenizer = AutoTokenizer.from_pretrained("Kaludi/autotrain-reviews-sentiment-analysis-3125888400", use_auth_token=True)
+tokenizer = AutoTokenizer.from_pretrained("Kaludi/Reviews-Sentiment-Analysis", use_auth_token=True)
 
-inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+inputs = tokenizer("I don't feel like you trust me to do my job.", return_tensors="pt")
 
 outputs = model(**inputs)
 ```