Update README.md

README.md

@@ -27,14 +27,14 @@ pipeline_tag: text-classification
 # BERT-based Sentiment Classification Mode
 
 ## Model Details
-- **Model Name:** tabularisai/bert-base-uncased-sentiment-five-classes
+- **Model Name:** tabularisai/robust_sentiment_analysis
 - **Base Model:** bert-base-uncased
 - **Task:** Text Classification (Sentiment Analysis)
 - **Language:** English
 
 ## Model Description
 
-This model is a fine-tuned version of `bert-base-uncased` for sentiment analysis. **Trained exclusively on syntethic data produced by SOTA LLMs: Llama3, Gemma2, and more**
+This model is a fine-tuned version of `bert-base-uncased` for sentiment analysis. **Trained only on syntethic data produced by SOTA LLMs: Llama3.1, Gemma2, and more**
 
 ### Training Data
 
@@ -62,7 +62,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
 # Load model and tokenizer
-model_name = "tabularisai/bert-base-uncased-sentiment-five-classes"
+model_name = "tabularisai/robust_sentiment_analysis"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
@@ -133,7 +133,7 @@ The model demonstrates strong performance across various sentiment categories. H
         env.allowLocalModels = false;
         env.useCDN = true;
 
-        const MODEL_NAME = 'tabularisai/bert-base-uncased-sentiment-five-classes';
+        const MODEL_NAME = 'tabularisai/robust_sentiment_analysis';
 
         function softmax(arr) {
             const max = Math.max(...arr);