Update README.md

README.md

@@ -1,7 +1,23 @@
+---
+license: mit
+datasets:
+- cardiffnlp/super_tweeteval
+language:
+- en
+pipeline_tag: text-classification
+---
 # cardiffnlp/twitter-roberta-base-latest-tempo-wic
 
-This is a [RoBERTa-base](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) model fine-tuned on [tempo-wic](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
+This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 and finetuned for meaning shift detection (binary classification) on the _TempoWIC_ dataset of [SuperTweetEval](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
+The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m).
 
+## Labels
+  "id2label": {
+    "0": "no",
+    "1": "yes"
+  }
+  
+## Example
 ```python
 from transformers import pipeline
 text_1 = "We don't like the search and frisk so this bitch in neutral"
@@ -11,4 +27,17 @@ text_input = f"{text_1}</s>{text_2}"
 pipe = pipeline('text-classification', model="cardiffnlp/twitter-roberta-base-latest-tempo-wic")
 pipe(text_input)
 >> [{'label': 'yes', 'score': 0.9994196891784668}]
-```
+```
+
+## Citation Information
+
+Please cite the [reference paper](https://arxiv.org/abs/2310.14757) if you use this model.
+
+```bibtex
+@inproceedings{antypas2023supertweeteval,
+  title={SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research},
+  author={Dimosthenis Antypas and Asahi Ushio and Francesco Barbieri and Leonardo Neves and Kiamehr Rezaee and Luis Espinosa-Anke and Jiaxin Pei and Jose Camacho-Collados},
+  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
+  year={2023}
+}
+```