| --- |
| datasets: |
| - cardiffnlp/tweet_topic_multi |
| metrics: |
| - f1 |
| - accuracy |
| 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 |
| base_model: roberta-base |
| 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: |
| - type: micro_f1_cardiffnlp/tweet_topic_multi |
| value: 0.7546616383825687 |
| name: Micro F1 (cardiffnlp/tweet_topic_multi) |
| - type: micro_f1_cardiffnlp/tweet_topic_multi |
| value: 0.5959450154471646 |
| name: Macro F1 (cardiffnlp/tweet_topic_multi) |
| - type: accuracy_cardiffnlp/tweet_topic_multi |
| value: 0.5318642048838594 |
| name: Accuracy (cardiffnlp/tweet_topic_multi) |
| --- |
| # 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", |
| } |
| ``` |
|
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