BeitTigreAI/tigre-data-fasttext

Tigre FastText Embeddings

This repository provides FastText word embeddings for the Tigre language.
The embeddings can be used for similarity, clustering, text classification, or as input features in downstream NLP tasks.

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📦 Installation

pip install gensim fasttext huggingface_hub

```python

from huggingface_hub import hf_hub_download
from gensim.models import KeyedVectors

# Download the vec file
vec_path = hf_hub_download(
    repo_id="BeitTigreAI/tigre-data-fasttext",
    filename="tigre.vec",
    repo_type="dataset"
)

# Load embeddings
model = KeyedVectors.load_word2vec_format(vec_path, binary=False)

# Example queries
print(model.most_similar("ቤት"))
print(model.most_similar("ዋልዳይት"))

```css
[('ወቤት', 0.54), ('ሐደክዉ', 0.50), ('ኢመሓዛትካ', 0.47), ...]
[('ዋልዳይትተ', 0.94), ('ዋልዳይትናመ', 0.93), ('ከዋልዳይት', 0.93), ...]

```python
from huggingface_hub import hf_hub_download
import fasttext

# Download the bin file
bin_path = hf_hub_download(
    repo_id="BeitTigreAI/tigre-data-fasttext",
    filename="tig.bin",
    repo_type="dataset"
)

# Load model
ft = fasttext.load_model(bin_path)

# Example queries
print(ft.get_word_vector("ሻም")[:10])
print(ft.get_nearest_neighbors("ሻም"))

```css
[-2.2306,  4.1328, -1.3079,  1.3905, -3.1971, -1.2134, 0.4555, -2.9989, -0.7958, -0.2645]
[(0.55, 'ሻማት'), (0.53, 'ዴሪር'), (0.46, 'ምልህዮት'), ...]