Instructions to use Phazel/fa_floret_400k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Phazel/fa_floret_400k with spaCy:
!pip install https://huggingface.co/Phazel/fa_floret_400k/resolve/main/fa_floret_400k-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("fa_floret_400k") # Importing as module. import fa_floret_400k nlp = fa_floret_400k.load() - Notebooks
- Google Colab
- Kaggle
| { | |
| "lang": "fa", | |
| "name": "floret_400k", | |
| "version": "0.1.0", | |
| "description": "Persian floret static vector table: 50,000 rows x 300 dimensions, floret mode, minn=maxn=5, hash_count=2, trained on 400,000 Persian documents. Vectors only, no pipeline components. This is the table used by the fa_*_news_md tier.", | |
| "author": "Kiyarash Fazeli", | |
| "email": "kiyarash@nlogn.ir", | |
| "url": "https://github.com/Fazel94/spacy-persian", | |
| "license": "CC BY-SA 4.0", | |
| "sources": [ | |
| { | |
| "name": "fa_floret static vectors (50k rows x 300d, floret mode, 400k Persian documents)", | |
| "url": "https://github.com/Fazel94/spacy-persian", | |
| "author": "Kiyarash Fazeli", | |
| "license": "CC BY-SA 4.0" | |
| } | |
| ], | |
| "spacy_version": ">=3.8.15,<3.9.0", | |
| "spacy_git_version": "f69c32f", | |
| "vectors": { | |
| "width": 300, | |
| "vectors": 50000, | |
| "keys": -1, | |
| "name": "floret" | |
| }, | |
| "labels": {}, | |
| "pipeline": [], | |
| "components": [], | |
| "disabled": [], | |
| "requirements": [ | |
| "spacy>=3.8.15,<3.9.0" | |
| ] | |
| } | |