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--- |
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language: en |
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widget: |
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- text: It is great to see athletes promoting awareness for climate change. |
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datasets: |
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- cardiffnlp/tweet_topic_multi |
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--- |
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# tweet-topic-21-multi |
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This model is based on a [TimeLMs](https://github.com/cardiffnlp/timelms) language model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for multi-label topic classification on a corpus of 11,267 [tweets](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is suitable for English. |
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- Reference Paper: [TweetTopic](https://arxiv.org/abs/2209.09824) (COLING 2022). |
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<b>Labels</b>: |
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| <span style="font-weight:normal">0: arts_&_culture</span> | <span style="font-weight:normal">5: fashion_&_style</span> | <span style="font-weight:normal">10: learning_&_educational</span> | <span style="font-weight:normal">15: science_&_technology</span> | |
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|-----------------------------|---------------------|----------------------------|--------------------------| |
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| 1: business_&_entrepreneurs | 6: film_tv_&_video | 11: music | 16: sports | |
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| 2: celebrity_&_pop_culture | 7: fitness_&_health | 12: news_&_social_concern | 17: travel_&_adventure | |
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| 3: diaries_&_daily_life | 8: food_&_dining | 13: other_hobbies | 18: youth_&_student_life | |
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| 4: family | 9: gaming | 14: relationships | | |
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## Full classification example |
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```python |
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from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification |
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from transformers import AutoTokenizer |
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import numpy as np |
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from scipy.special import expit |
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MODEL = f"cardiffnlp/tweet-topic-21-multi" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL) |
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# PT |
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model = AutoModelForSequenceClassification.from_pretrained(MODEL) |
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class_mapping = model.config.id2label |
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text = "It is great to see athletes promoting awareness for climate change." |
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tokens = tokenizer(text, return_tensors='pt') |
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output = model(**tokens) |
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scores = output[0][0].detach().numpy() |
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scores = expit(scores) |
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predictions = (scores >= 0.5) * 1 |
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# TF |
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#tf_model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) |
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#class_mapping = tf_model.config.id2label |
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#text = "It is great to see athletes promoting awareness for climate change." |
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#tokens = tokenizer(text, return_tensors='tf') |
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#output = tf_model(**tokens) |
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#scores = output[0][0] |
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#scores = expit(scores) |
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#predictions = (scores >= 0.5) * 1 |
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# Map to classes |
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for i in range(len(predictions)): |
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if predictions[i]: |
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print(class_mapping[i]) |
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``` |
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Output: |
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``` |
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news_&_social_concern |
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sports |
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``` |
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### BibTeX entry and citation info |
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Please cite the [reference paper](https://aclanthology.org/2022.coling-1.299/) if you use this model. |
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```bibtex |
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@inproceedings{antypas-etal-2022-twitter, |
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title = "{T}witter Topic Classification", |
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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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Silva, Vitor and |
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Neves, Leonardo 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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url = "https://aclanthology.org/2022.coling-1.299", |
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pages = "3386--3400" |
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} |
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``` |
