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--- |
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datasets: |
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- cardiffnlp/tweet_topic_multi |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020 |
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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: cardiffnlp/tweet_topic_multi |
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type: cardiffnlp/tweet_topic_multi |
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args: cardiffnlp/tweet_topic_multi |
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split: test_2021 |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.7294463087248323 |
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- name: F1 (macro) |
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type: f1_macro |
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value: 0.5347990396677114 |
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- name: Accuracy |
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type: accuracy |
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value: 0.5026801667659321 |
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pipeline_tag: text-classification |
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widget: |
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- 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" |
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example_title: "Example 1" |
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- 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." |
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example_title: "Example 2" |
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--- |
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# cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020 |
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|
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic. |
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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: |
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- F1 (micro): 0.7294463087248323 |
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- F1 (macro): 0.5347990396677114 |
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- Accuracy: 0.5026801667659321 |
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### Usage |
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|
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```python |
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import math |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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|
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def sigmoid(x): |
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return 1 / (1 + math.exp(-x)) |
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|
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tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020") |
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model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020", problem_type="multi_label_classification") |
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model.eval() |
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class_mapping = model.config.id2label |
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with torch.no_grad(): |
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text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}} |
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tokens = tokenizer(text, return_tensors='pt') |
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output = model(**tokens) |
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flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()] |
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topic = [class_mapping[n] for n, i in enumerate(flags) if i] |
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print(topic) |
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``` |
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### Reference |
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|
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``` |
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@inproceedings{dimosthenis-etal-2022-twitter, |
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title = "{T}witter {T}opic {C}lassification", |
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author = "Antypas, Dimosthenis and |
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Ushio, Asahi and |
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Camacho-Collados, Jose and |
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Neves, Leonardo and |
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Silva, Vitor and |
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Barbieri, Francesco", |
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
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month = oct, |
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year = "2022", |
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address = "Gyeongju, Republic of Korea", |
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publisher = "International Committee on Computational Linguistics" |
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} |
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``` |
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