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--- |
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language: |
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- en |
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license: mit |
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datasets: |
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- cardiffnlp/super_tweeteval |
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--- |
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# cardiffnlp/twitter-roberta-base-intimacy-latest |
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This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 and finetuned for intimacy analysis (regression on a single text) on the _TweetIntimacy_ dataset of [SuperTweetEval](https://huggingface.co./datasets/cardiffnlp/super_tweeteval). |
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The original Twitter-based RoBERTa model can be found [here](https://huggingface.co./cardiffnlp/twitter-roberta-base-2022-154m). |
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## Example |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model_name = "cardiffnlp/twitter-roberta-base-intimacy-latest" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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text= '@user Furthermore, harassment is ILLEGAL in any form!' |
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model(**tokenizer(text, return_tensors="pt")).logits |
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>> tensor([1.6744]) |
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``` |
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## Citation Information |
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Please cite the [reference paper](https://arxiv.org/abs/2310.14757) if you use this model. |
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```bibtex |
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@inproceedings{antypas2023supertweeteval, |
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title={SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research}, |
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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}, |
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booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023}, |
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year={2023} |
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} |
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``` |
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