Update README.md

README.md

@@ -4,9 +4,9 @@ datasets:
 language:
 - en
 ---
-# Twitter-roBERTa-base for Sentiment Analysis
+# Twitter-roBERTa for Sentiment Analysis
 
-This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)).
+This is a roBERTa model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English.
 
 - Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
 - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
@@ -16,8 +16,6 @@ This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment
 1 -> Neutral;
 2 -> Positive
 
-<b>New!</b> We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets. 
-See [twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) and [TweetNLP](https://tweetnlp.org) for more details.
 
 ## Example of classification
 
@@ -46,13 +44,13 @@ def preprocess(text):
 # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
 
 task='sentiment'
-MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
+MODEL = f"researchworkai/Sentiment-roBERTa-Twitter-{task}"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
 # download label mapping
 labels=[]
-mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
+mapping_link = f"https://raw.githubusercontent.com/researchworkai/tweeteval/main/datasets/{task}/mapping.txt"
 with urllib.request.urlopen(mapping_link) as f:
     html = f.read().decode('utf-8').split("\n")
     csvreader = csv.reader(html, delimiter='\t')