Add dataset

README.md

@@ -1,24 +1,27 @@
 ---
-language: english
+language: en
 widget:
-- text: "Covid cases are increasing fast!"
-- text: "🤗"
-- text: "I hate you 🤮"
+- text: Covid cases are increasing fast!
+datasets:
+- tweet_eval
 ---
 
 
-# Twitter-roBERTa-base for Sentiment Analysis
+# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
 
-This is a roBERTa-base model trained on ~200M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English.
+This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. 
+The original Twitter-based RoBERTa 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: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
-- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
+- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829). 
+- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
 
 <b>Labels</b>: 
 0 -> Negative;
 1 -> Neutral;
 2 -> Positive
 
+This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).
+
 ## Example Pipeline
 ```python
 from transformers import pipeline

config.json

@@ -12,16 +12,16 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "Negative",
-    "1": "Neutral",
-    "2": "Positive"
+    "0": "negative",
+    "1": "neutral",
+    "2": "positive"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "Negative": 0,
-    "Neutral": 1,
-    "Positive": 2
+    "negative": 0,
+    "neutral": 1,
+    "positive": 2
   },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,