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.
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",
}
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Dataset used to train cardiffnlp/roberta-base-topic-single
Evaluation results
- Micro F1 (cardiffnlp/tweet_topic_single) on cardiffnlp/tweet_topic_singleself-reported0.882
- Macro F1 (cardiffnlp/tweet_topic_single) on cardiffnlp/tweet_topic_singleself-reported0.736
- Accuracy (cardiffnlp/tweet_topic_single) on cardiffnlp/tweet_topic_singleself-reported0.882