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--- |
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datasets: |
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- tweet_eval |
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metrics: |
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- f1 |
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- accuracy |
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pipeline_tag: text-classification |
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widget: |
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- text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} |
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via {@bluenoterecords@} link below {{URL}} |
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example_title: topic_classification 1 |
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- text: Yes, including Medicare and social security saving👍 |
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example_title: sentiment 1 |
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- text: All two of them taste like ass. |
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example_title: offensive 1 |
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- text: If you wanna look like a badass, have drama on social media |
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example_title: irony 1 |
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- text: Whoever just unfollowed me you a bitch |
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example_title: hate 1 |
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- text: I love swimming for the same reason I love meditating...the feeling of weightlessness. |
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example_title: emotion 1 |
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- text: Beautiful sunset last night from the pontoon @TupperLakeNY |
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example_title: emoji 1 |
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base_model: cardiffnlp/twitter-roberta-base-dec2021 |
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model-index: |
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- name: cardiffnlp/twitter-roberta-base-dec2021-sentiment |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: tweet_eval |
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type: sentiment |
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split: test |
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metrics: |
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- type: micro_f1_tweet_eval/sentiment |
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value: 0.7128785411917942 |
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name: Micro F1 (tweet_eval/sentiment) |
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- type: micro_f1_tweet_eval/sentiment |
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value: 0.7149679965048391 |
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name: Macro F1 (tweet_eval/sentiment) |
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- type: accuracy_tweet_eval/sentiment |
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value: 0.7128785411917942 |
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name: Accuracy (tweet_eval/sentiment) |
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--- |
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# cardiffnlp/twitter-roberta-base-dec2021-sentiment |
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|
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the |
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[`tweet_eval (sentiment)`](https://huggingface.co/datasets/tweet_eval) |
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via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp). |
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Training split is `train` and parameters have been tuned on the validation split `validation`. |
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Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-sentiment/raw/main/metric.json)). |
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- F1 (micro): 0.7128785411917942 |
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- F1 (macro): 0.7149679965048391 |
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- Accuracy: 0.7128785411917942 |
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### Usage |
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Install tweetnlp via pip. |
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```shell |
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pip install tweetnlp |
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``` |
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Load the model in python. |
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```python |
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import tweetnlp |
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model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-dec2021-sentiment", max_length=128) |
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model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}') |
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``` |
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### Reference |
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|
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``` |
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@inproceedings{camacho-collados-etal-2022-tweetnlp, |
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title={{T}weet{NLP}: {C}utting-{E}dge {N}atural {L}anguage {P}rocessing for {S}ocial {M}edia}, |
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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}, |
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author = "Ushio, Asahi and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", |
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month = nov, |
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year = "2022", |
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address = "Abu Dhabi, U.A.E.", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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