Update README.md

README.md

@@ -1,22 +1,15 @@
 ---
 tags:
-- autotrain
 - text-classification
+- sentiment-analysis
 language:
 - en
 widget:
-- text: "I love AutoTrain 🤗"
+- text: "I love this product! One of my best purchases this year."
 datasets:
-- madmancity/autotrain-data-revmlc
-co2_eq_emissions:
-  emissions: 0.8759779776754995
+- madmancity/revmlc
 ---
 
-# Model Trained Using AutoTrain
-
-- Problem type: Multi-class Classification
-- Model ID: 48079117239
-- CO2 Emissions (in grams): 0.8760
 
 ## Validation Metrics
 
@@ -38,7 +31,7 @@ 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/madmancity/autotrain-revmlc-48079117239
+$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love this product! One of my best purchases this year."}' https://api-inference.huggingface.co/models/madmancity/revmlc
 ```
 
 Or Python API:
@@ -46,11 +39,11 @@ Or Python API:
 ```
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-model = AutoModelForSequenceClassification.from_pretrained("madmancity/autotrain-revmlc-48079117239", use_auth_token=True)
+model = AutoModelForSequenceClassification.from_pretrained("madmancity/revmlc", use_auth_token=True)
 
-tokenizer = AutoTokenizer.from_pretrained("madmancity/autotrain-revmlc-48079117239", use_auth_token=True)
+tokenizer = AutoTokenizer.from_pretrained("madmancity/revmlc", use_auth_token=True)
 
-inputs = tokenizer("I love AutoTrain", return_tensors="pt")
+inputs = tokenizer("I love this product! One of my best purchases this year.", return_tensors="pt")
 
 outputs = model(**inputs)
 ```