metadata

datasets:
  - tweet_eval
metrics:
  - f1
  - accuracy
model-index:
  - name: cardiffnlp/twitter-roberta-base-dec2021-sentiment
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: tweet_eval
          type: sentiment
          split: test
        metrics:
          - name: Micro F1 (tweet_eval/sentiment)
            type: micro_f1_tweet_eval/sentiment
            value: 0.7128785411917942
          - name: Macro F1 (tweet_eval/sentiment)
            type: micro_f1_tweet_eval/sentiment
            value: 0.7149679965048391
          - name: Accuracy (tweet_eval/sentiment)
            type: accuracy_tweet_eval/sentiment
            value: 0.7128785411917942
pipeline_tag: text-classification
widget:
  - text: >-
      Get the all-analog Classic Vinyl Edition of "Takin Off" Album from
      {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}
    example_title: topic_classification 1
  - text: Yes, including Medicare and social security saving👍
    example_title: sentiment 1
  - text: All two of them taste like ass.
    example_title: offensive 1
  - text: If you wanna look like a badass, have drama on social media
    example_title: irony 1
  - text: Whoever just unfollowed me you a bitch
    example_title: hate 1
  - text: >-
      I love swimming for the same reason I love meditating...the feeling of
      weightlessness.
    example_title: emotion 1
  - text: Beautiful sunset last night from the pontoon @TupperLakeNY
    example_title: emoji 1

cardiffnlp/twitter-roberta-base-dec2021-sentiment

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the tweet_eval (sentiment) via tweetnlp. Training split is train and parameters have been tuned on the validation split validation.

Following metrics are achieved on the test split test (link).

F1 (micro): 0.7128785411917942
F1 (macro): 0.7149679965048391
Accuracy: 0.7128785411917942

Usage

Install tweetnlp via pip.

pip install tweetnlp

Load the model in python.

import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-dec2021-sentiment", max_length=128)
model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}')

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"
}