Update README.md

README.md

@@ -1,17 +1,14 @@
 ---
-language: en
+language: english
 widget:
-- text: Covid cases are increasing fast!
-datasets:
-- tweet_eval
-license: cc-by-4.0
+- text: "Covid cases are increasing fast!"
 ---
 
 
-# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
+# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2021)
 
-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. 
+This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for sentiment analysis with the TweetEval benchmark. 
+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). 
 - Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
@@ -21,8 +18,6 @@ The original Twitter-based RoBERTa model can be found [here](https://huggingface
 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
@@ -84,50 +79,4 @@ Output:
 1) Negative 0.7236
 2) Neutral 0.2287
 3) Positive 0.0477
-```
-
-
-### References 
-```
-@inproceedings{camacho-collados-etal-2022-tweetnlp,
-    title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
-    author = "Camacho-collados, Jose  and
-      Rezaee, Kiamehr  and
-      Riahi, Talayeh  and
-      Ushio, Asahi  and
-      Loureiro, Daniel  and
-      Antypas, Dimosthenis  and
-      Boisson, Joanne  and
-      Espinosa Anke, Luis  and
-      Liu, Fangyu  and
-      Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
-    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = dec,
-    year = "2022",
-    address = "Abu Dhabi, UAE",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.emnlp-demos.5",
-    pages = "38--49"
-}
-
-```
-
-```
-@inproceedings{loureiro-etal-2022-timelms,
-    title = "{T}ime{LM}s: Diachronic Language Models from {T}witter",
-    author = "Loureiro, Daniel  and
-      Barbieri, Francesco  and
-      Neves, Leonardo  and
-      Espinosa Anke, Luis  and
-      Camacho-collados, Jose",
-    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
-    month = may,
-    year = "2022",
-    address = "Dublin, Ireland",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.acl-demo.25",
-    doi = "10.18653/v1/2022.acl-demo.25",
-    pages = "251--260"
-}
-
 ```

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,