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cardiffnlp/
roberta-base-topic-single

Name: roberta-base-topic-single
Availability: InStock

cardiffnlp/tweet_topic_single

model-index

autotrain_compatible

endpoints_compatible

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cardiffnlp/roberta-base-topic-single

This model is a fine-tuned version of roberta-base on the cardiffnlp/tweet_topic_single via tweetnlp. Training split is train_all and parameters have been tuned on the validation split validation_2021.

Following metrics are achieved on the test split test_2021 (link).

F1 (micro): 0.8818665091553456
F1 (macro): 0.7359303318518903
Accuracy: 0.8818665091553456

Usage

Install tweetnlp via pip.

pip install tweetnlp

Load the model in python.

python

import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/roberta-base-topic-single", 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}: {C}utting-{E}dge {N}atural {L}anguage {P}rocessing for {S}ocial {M}edia},
    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},
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}