--- datasets: - cardiffnlp/tweet_topic_multi metrics: - f1 - accuracy model-index: - name: cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all results: - task: type: text-classification name: Text Classification dataset: name: cardiffnlp/tweet_topic_multi type: cardiffnlp/tweet_topic_multi args: cardiffnlp/tweet_topic_multi split: test_2021 metrics: - name: F1 type: f1 value: 0.7647668393782383 - name: F1 (macro) type: f1_macro value: 0.6187022581213811 - name: Accuracy type: accuracy value: 0.5485407980941036 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/twitter-roberta-base-dec2021-tweet-topic-multi-all This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic. Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set: - F1 (micro): 0.7647668393782383 - F1 (macro): 0.6187022581213811 - Accuracy: 0.5485407980941036 ### Usage ```python import math import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer def sigmoid(x): return 1 / (1 + math.exp(-x)) tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all") model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all", problem_type="multi_label_classification") model.eval() class_mapping = model.config.id2label with torch.no_grad(): text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}} tokens = tokenizer(text, return_tensors='pt') output = model(**tokens) flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()] topic = [class_mapping[n] for n, i in enumerate(flags) if i] 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" } ```