README.md

---
language:
- en
license: mit
datasets:
- cardiffnlp/super_tweeteval
pipeline_tag: text-classification
widget:
- text: "In this bullpen, you should be able to ask why and understand why we do the things we do.' @Trisha_Ford 😍 #pitchstock2020 @user</s>Castro needs to be the last bullpen guy to pitch.</s>bullpen"
---
# cardiffnlp/twitter-roberta-base-tempo-wic-latest

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 = "'In this bullpen, you should be able to ask why and understand why we do the things we do.' @Trisha_Ford 😍 #pitchstock2020 @user"
text_2 = "Castro needs to be the last bullpen guy to pitch."
target = "bullpen"
text_input = f"{text_1}</s>{text_2}</s>{target}"

pipe = pipeline('text-classification', model="cardiffnlp/twitter-roberta-base-tempo-wic-latest")
pipe(text_input)
>> [{'label': 'yes', 'score': 0.9964596629142761}]
```

## 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}
}
```