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datasets: cardiffnlp/tweet_topic_multi |
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--- |
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# tweet-topic-19-multi |
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This is a RoBERTa-base model trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)) and finetuned for multi-label topic classification on a corpus of 11,267 [tweets](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). |
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The original RoBERTa-base model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. |
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- Reference Papers: [TimeLMs paper](https://arxiv.org/abs/2202.03829), [TweetTopic](https://arxiv.org/abs/2209.09824). |
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- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms). |
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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-19-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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``` |
