metadata

language:
  - en
license: mit
datasets:
  - cardiffnlp/super_tweeteval
pipeline_tag: token-classification

cardiffnlp/twitter-roberta-base-ner7-latest

This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 and finetuned for topic Name Entity Recognition on the TweetNER7 dataset of SuperTweetEval. The original Twitter-based RoBERTa model can be found here.

Labels


  "id2label": {
    "0": "B-corporation",
    "1": "B-creative_work",
    "2": "B-event",
    "3": "B-group",
    "4": "B-location",
    "5": "B-person",
    "6": "B-product",
    "7": "I-corporation",
    "8": "I-creative_work",
    "9": "I-event",
    "10": "I-group",
    "11": "I-location",
    "12": "I-person",
    "13": "I-product",
    "14": "O"
  }

Example

from transformers import pipeline
text = "Halo Infinite analysis - The only true analysis {{USERNAME}} {{USERNAME}} {{USERNAME}} {{USERNAME}} {{USERNAME}} {{URL}}"

model_name = "cardiffnlp/twitter-roberta-base-ner7-latest"
pipe = pipeline('ner', model=model_name, tokenizer=model_name, aggregation_strategy="simple")
predictions = pipe(text)
predictions
>> [{'entity_group': 'creative_work',
      'score': 0.5278398,
      'word': 'Halo Infinite',
      'start': 0,
      'end': 13}]

Citation Information

Please cite the reference paper if you use this model.

@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}
}