metadata

datasets:
  - cardiffnlp/tweet_sentiment_multilingual
metrics:
  - f1
  - accuracy
model-index:
  - name: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: cardiffnlp/tweet_sentiment_multilingual
          type: all
          split: test
        metrics:
          - name: Micro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
            type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
            value: 0.6931034482758621
          - name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
            type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
            value: 0.692628774202147
          - name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all)
            type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all
            value: 0.6931034482758621
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-xlm-roberta-base-sentiment-multilingual

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base on the cardiffnlp/tweet_sentiment_multilingual (all) 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): 0.6931034482758621
F1 (macro): 0.692628774202147
Accuracy: 0.6931034482758621

Usage

Install tweetnlp via pip.

pip install tweetnlp

Load the model in python.

import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual", 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{camacho-collados-etal-2022-tweetnlp,
    title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
    author = "Camacho-collados, Jose  and
      Rezaee, Kiamehr  and
      Riahi, Talayeh  and
      Ushio, Asahi  and
      Loureiro, Daniel  and
      Antypas, Dimosthenis  and
      Boisson, Joanne  and
      Espinosa Anke, Luis  and
      Liu, Fangyu  and
      Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-demos.5",
    pages = "38--49"
}