You can deploy this model and start earning money today!

cardiffnlp/
twitter-xlm-roberta-base-sentiment-multilingual

Name: twitter-xlm-roberta-base-sentiment-multilingual
Availability: InStock

cardiffnlp/tweet_sentiment_multilingual

model-index

autotrain_compatible

endpoints_compatible

Views

No views yet

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.

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"
}