Update README.md

README.md

@@ -1,9 +1,9 @@
 # tweet-topic-19-multi
 
-This is a roBERTa-base model  trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)), and finetuned for multi-label topic classification on a corpus of 11,267 tweets.
-The original roBERTa-base model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. 
+This is a RoBERTa-base model trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)) and finetuned for multi-label topic classification on a corpus of 11,267 [tweets](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi).
+The original RoBERTa-base model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. 
 
-- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829). 
+- Reference Papers: [TimeLMs paper](https://arxiv.org/abs/2202.03829), [TweetTopic](https://arxiv.org/abs/2209.09824). 
 - Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
 
 <b>Labels</b>: 
@@ -43,7 +43,7 @@ predictions = (scores >= 0.5) * 1
 
 # TF
 #tf_model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
-#class_mapping = model.config.id2label
+#class_mapping = tf_model.config.id2label
 #text = "It is great to see athletes promoting awareness for climate change."
 #tokens = tokenizer(text, return_tensors='tf')
 #output = tf_model(**tokens)