Instructions to use cardiffnlp/roberta-base-topic-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cardiffnlp/roberta-base-topic-multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cardiffnlp/roberta-base-topic-multi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/roberta-base-topic-multi") model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/roberta-base-topic-multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from cardiffnlp/roberta-base-topic-multi: direct link, hf CLI and curl.
- Browser
- Download file 3.28 kB
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https://huggingface.co/cardiffnlp/roberta-base-topic-multi/resolve/main/README.md
- Command line
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hf download hf://cardiffnlp/roberta-base-topic-multi/README.md
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curl -L -o README.md https://huggingface.co/cardiffnlp/roberta-base-topic-multi/resolve/main/README.md
3.28 kB
| datasets: | |
| - cardiffnlp/tweet_topic_multi | |
| metrics: | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: cardiffnlp/roberta-base-topic-multi | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: cardiffnlp/tweet_topic_multi | |
| type: cardiffnlp/tweet_topic_multi | |
| split: test_2021 | |
| metrics: | |
| - name: Micro F1 (cardiffnlp/tweet_topic_multi) | |
| type: micro_f1_cardiffnlp/tweet_topic_multi | |
| value: 0.7546616383825687 | |
| - name: Macro F1 (cardiffnlp/tweet_topic_multi) | |
| type: micro_f1_cardiffnlp/tweet_topic_multi | |
| value: 0.5959450154471646 | |
| - name: Accuracy (cardiffnlp/tweet_topic_multi) | |
| type: accuracy_cardiffnlp/tweet_topic_multi | |
| value: 0.5318642048838594 | |
| pipeline_tag: text-classification | |
| widget: | |
| - text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}} | |
| example_title: "topic_classification 1" | |
| - text: Yes, including Medicare and social security saving👍 | |
| example_title: "sentiment 1" | |
| - text: All two of them taste like ass. | |
| example_title: "offensive 1" | |
| - text: If you wanna look like a badass, have drama on social media | |
| example_title: "irony 1" | |
| - text: Whoever just unfollowed me you a bitch | |
| example_title: "hate 1" | |
| - text: I love swimming for the same reason I love meditating...the feeling of weightlessness. | |
| example_title: "emotion 1" | |
| - text: Beautiful sunset last night from the pontoon @TupperLakeNY | |
| example_title: "emoji 1" | |
| # cardiffnlp/roberta-base-topic-multi | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the | |
| [`cardiffnlp/tweet_topic_multi`](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) | |
| via [`tweetnlp`](https://github.com/cardiffnlp/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](https://huggingface.co/cardiffnlp/roberta-base-topic-multi/raw/main/metric.json)). | |
| - F1 (micro): 0.7546616383825687 | |
| - F1 (macro): 0.5959450154471646 | |
| - Accuracy: 0.5318642048838594 | |
| ### Usage | |
| Install tweetnlp via pip. | |
| ```shell | |
| pip install tweetnlp | |
| ``` | |
| Load the model in python. | |
| ```python | |
| import tweetnlp | |
| model = tweetnlp.Classifier("cardiffnlp/roberta-base-topic-multi", 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", | |
| } | |
| ``` | |