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  license: cc-by-sa-4.0
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  language:
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  - tig
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- pip install gensim
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- pip install fasttext huggingface_hub
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- from huggingface_hub import hf_hub_download
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- import fasttext
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  from huggingface_hub import hf_hub_download
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  from gensim.models import KeyedVectors
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  vec_path = hf_hub_download(
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  repo_id="BeitTigreAI/tigre-data-fasttext",
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  filename="tigre.vec",
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- repo_type="dataset"
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  )
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  model = KeyedVectors.load_word2vec_format(vec_path, binary=False)
 
 
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  print(model.most_similar("ቤት"))
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  print(model.most_similar("ዋልዳይት"))
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- ---
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- [('ወቤት', 0.5403133630752563), ('ሐደክዉ', 0.5009863972663879), ('ኢመሓዛትካ', 0.4794275760650635), ('ለቤት', 0.47765740752220154), ('ከቤት', 0.4470037817955017), ('ለብርሂ', 0.41692110896110535), ('ከሺነት', 0.4148196578025818), ('ከሺነትነ', 0.41287708282470703), ('ወንጌላይት', 0.410333514213562), ('ወንጌላይ', 0.40985122323036194)]
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- [('ዋልዳይትተ', 0.9436762928962708), ('ዋልዳይትናመ', 0.9374170303344727), ('ከዋልዳይት', 0.9302672147750854), ('ዋልዳይትኪ', 0.9297484755516052), ('ዋልዳይትቼ', 0.922221302986145), ('ዋልዳይትነ', 0.9221974611282349), ('ዋልዳይትመ', 0.9156748652458191), ('ዋልዳይትኩም', 0.9076657891273499), ('ዋልዳይትዬ', 0.8774775266647339), ('ዋልዳይትከ', 0.8705683350563049)]
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- ---
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-
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- from huggingface_hub import hf_hub_download
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- import fasttext
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-
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- bin_path = hf_hub_download(
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- repo_id="BeitTigreAI/tigre-data-fasttext",
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- filename="tig.bin",
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- repo_type="dataset"
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- )
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-
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- ft = fasttext.load_model(bin_path)
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-
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- print(ft.get_word_vector("ሻም")[:10])
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- print(ft.get_nearest_neighbors("ሻም"))
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-
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- ---
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-
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- [-2.2306004 4.1328444 -1.307913 1.390595 -3.1971953 -1.213482
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- 0.45558256 -2.998946 -0.79585505 -0.26456892]
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- [(0.5539113879203796, 'ሻማት'), (0.5307868719100952, 'ዴሪር'), (0.4694315791130066, 'ሕዋሌሻም'), (0.46039608120918274, 'ምልህዮት'), (0.45820483565330505, 'ወሻም'), (0.45434507727622986, 'ከምልህዮት'), (0.45119181275367737, 'ለምልህዮት'), (0.4460937976837158, 'ሻሙ'), (0.43825647234916687, 'ሻሞም'), (0.4371178150177002, 'ወረአየት')]
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  license: cc-by-sa-4.0
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  language:
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  - tig
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+ tags:
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+ - fasttext
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+ - word-embeddings
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+ - tigre
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+ ---
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+
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+ # Tigre FastText Embeddings
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+
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+ This repository provides **FastText word embeddings for the Tigre language**.
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+ The embeddings can be used for similarity, clustering, text classification, or as input features in downstream NLP tasks.
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+
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  ---
 
 
 
 
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+ ## 📦 Installation
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+
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+ ```bash
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+ pip install gensim fasttext huggingface_hub
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+
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+ ```python
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  from huggingface_hub import hf_hub_download
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  from gensim.models import KeyedVectors
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+ # Download the vec file
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  vec_path = hf_hub_download(
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  repo_id="BeitTigreAI/tigre-data-fasttext",
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  filename="tigre.vec",
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+ repo_type="dataset"
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  )
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+ # Load embeddings
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  model = KeyedVectors.load_word2vec_format(vec_path, binary=False)
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+
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+ # Example queries
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  print(model.most_similar("ቤት"))
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  print(model.most_similar("ዋልዳይት"))
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+ ```css
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+ [('ወቤት', 0.54), ('ሐደክዉ', 0.50), ('ኢመሓዛትካ', 0.47), ...]
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+ [('ዋልዳይትተ', 0.94), ('ዋልዳይትናመ', 0.93), ('ከዋልዳይት', 0.93), ...]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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