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--- |
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license: bsd-3-clause |
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language: |
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- zh |
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- en |
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- id |
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- ja |
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- es |
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--- |
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# TUBELEX FastText Word Embeddings |
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FastText Word Embeddings trained on the TUBELEX YouTube subtitle corpora. We use the 300-dimensional [fastText](https://github.com/facebookresearch/fastText) CBOW model with position weights, 10 negative samples, 10 epochs, character 5-grams (other paramters: default) ([Grave et al., 2018](https://aclanthology.org/L18-1550)). |
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We provide both '\*.bin' files (for fastText) and '\*.vec' files that follow the common Word2vec format, and can be used for instance with the `gensim` package. |
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# What is TUBELEX? |
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TUBELEX is a YouTube subtitle corpus currently available for Chinese, English, Indonesian, Japanese, and Spanish. |
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- TODO: paper link |
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- [KenLM n-gram models](https://huggingface.co/naist-nlp/tubelex-kenlm) |
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- [word frequencies and code](https://github.com/naist-nlp/tubelex) |
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# Usage |
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To download and use the fastText models in Python, first install dependencies: |
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``` |
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pip install huggingface_hub |
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pip install fasttext |
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``` |
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You can then use e.g. the English (`en`) model in the following way: |
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``` |
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import fasttext |
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from huggingface_hub import hf_hub_download |
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model_file = hf_hub_download(repo_id='naist-nlp/tubelex-kenlm', filename='tubelex-en.bin') |
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model = fasttext.load_model(model_file) |
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print(model['koala']) |
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``` |
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