--- datasets: - cardiffnlp/tweet_topic_single metrics: - f1 - accuracy model-index: - name: cardiffnlp/roberta-large-tweet-topic-single-2020 results: - task: type: text-classification name: Text Classification dataset: name: cardiffnlp/tweet_topic_single type: cardiffnlp/tweet_topic_single args: cardiffnlp/tweet_topic_single split: test_2021 metrics: - name: F1 type: f1 value: 0.8789131718842291 - name: F1 (macro) type: f1_macro value: 0.7056499344872201 - name: Accuracy type: accuracy value: 0.8789131718842291 pipeline_tag: text-classification widget: - text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys" example_title: "Example 1" - text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US." example_title: "Example 2" --- # cardiffnlp/roberta-large-tweet-topic-single-2020 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic. Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set: - F1 (micro): 0.8789131718842291 - F1 (macro): 0.7056499344872201 - Accuracy: 0.8789131718842291 ### Usage ```python from transformers import pipeline pipe = pipeline("text-classification", "cardiffnlp/roberta-large-tweet-topic-single-2020") topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.") print(topic) ``` ### 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" } ```