| --- |
| language: |
| - ar |
| pipeline_tag: feature-extraction |
| --- |
| We have successfully trained a [FastText](https://fasttext.cc/)-based Word2Vec model on our dataset, utilizing an embedding size of 100 dimensions. |
| This model is designed to generate vector representations for individual words and sub-words, allowing it to effectively capture semantic and morphological relationships within the text. \ |
| To obtain representations at the sentence level, we compute embeddings for all constituent words and sub-words in a given text and then apply averaging. |
| This approach ensures that the resulting sentence embedding encapsulates the overall meaning and preserving contextual nuances. |
