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--- |
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language: |
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- en |
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- es |
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- ja |
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- el |
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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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- cardiffnlp/tweet_topic_multilingual |
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license: mit |
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metrics: |
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- f1 |
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pipeline_tag: text-classification |
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--- |
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# tweet-topic-large-multilingual |
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This model is based on [cardiffnlp/twitter-xlm-roberta-large-2022](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-large-2022) language model and isfinetuned for multi-label topic classification in English, Spanish, Japanese, and Greek. |
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The models is trained using [TweetTopic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) and [X-Topic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multilingual) datasets (see main [EMNLP 2024 reference paper](https://arxiv.org/abs/2410.03075). |
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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-large-multilingual" |
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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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## Results on X-Topic |
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| | English | Spanish | Japanese | Greek | |
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|--------------|---------|---------|----------|-------| |
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| **Macro-F1** | 60.2 | 52.9 | 57.3 | 50.3 | |
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| **Micro-F1** | 66.3 | 67.0 | 61.4 | 73.0 | |
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## BibTeX entry and citation info |
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TBA |
