README.md

metadata

datasets:
  - cardiffnlp/tweet_topic_single
metrics:
  - f1
  - accuracy
model-index:
  - name: cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: cardiffnlp/tweet_topic_single
          type: cardiffnlp/tweet_topic_single
          args: cardiffnlp/tweet_topic_single
          split: test_2021
        metrics:
          - name: F1
            type: f1
            value: 0.8924985233313645
          - name: F1 (macro)
            type: f1_macro
            value: 0.7744939280307456
          - name: Accuracy
            type: accuracy
            value: 0.8924985233313645
pipeline_tag: text-classification
widget:
  - text: >-
      I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but
      man does their experience versus the Blue Jackets this year and last help
      them a lot versus this Islanders team. Another meat grinder upcoming for
      the good guys
    example_title: Example 1
  - text: >-
      Love to take night time bike rides at the jersey shore. Seaside Heights
      boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in
      the US.
    example_title: Example 2

cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2019-90m on the tweet_topic_single. This model is fine-tuned on train_all split and validated on test_2021 split of tweet_topic. Fine-tuning script can be found here. It achieves the following results on the test_2021 set:

• F1 (micro): 0.8924985233313645
• F1 (macro): 0.7744939280307456
• Accuracy: 0.8924985233313645

Usage

from transformers import pipeline

pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all")  
topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
print(topic)

Reference

@inproceedings{dimosthenis-etal-2022-twitter,
    title = "{T}witter {T}opic {C}lassification",
    author = "Antypas, Dimosthenis  and
    Ushio, Asahi  and
    Camacho-Collados, Jose  and
    Neves, Leonardo  and
    Silva, Vitor  and
    Barbieri, Francesco",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics"
}