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
File size: 2,474 Bytes
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language:
- fa
license: cc-by-sa-4.0
library_name: spacy
pipeline_tag: feature-extraction
tags:
- spacy
- feature-extraction
- floret
- word-embeddings
- persian
- farsi
---
# fa_floret_400k
Persian floret static vector table: 50,000 rows x 300 dimensions, `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_dep_news_md` / `fa_core_news_md` / `fa_ent_news_md` tier.
Because floret hashes subwords rather than storing whole-word keys, there are no OOV tokens:
every string gets a vector, which is what makes it usable on Persian text where inconsistent
ZWNJ placement otherwise explodes the vocabulary.
## Install
```bash
pip install https://huggingface.co/Phazel/fa_floret_400k/resolve/main/fa_floret_400k-0.1.0-py3-none-any.whl
```
## Use
```python
import spacy
nlp = spacy.load("fa_floret_400k") # vectors only: nlp.pipe_names == []
print(nlp.vocab.vectors.shape) # (50000, 300)
print(nlp("کتابهای").vector.shape) # (300,)
```
To train a pipeline against this table, pass it to spaCy's `--paths.vectors` and set
`components.tok2vec.model.embed.include_static_vectors = true`:
```bash
python -m spacy train config.cfg --paths.vectors fa_floret_400k
```
## Vector table
| Property | Value |
| --- | --- |
| Rows | 50,000 |
| Dimensions | 300 |
| Mode | floret (subword, Bloom-hashed) |
| `minn` / `maxn` | 5 / 5 |
| `hash_count` | 2 |
| Training corpus | 400,000 Persian documents |
## Related
| Package | Rows | Corpus | Used by |
| --- | ---: | --- | --- |
| [`fa_floret_400k`](https://huggingface.co/Phazel/fa_floret_400k) | 50k | 400k Persian documents | `md` tier |
| [`fa_floret_full_wiki`](https://huggingface.co/Phazel/fa_floret_full_wiki) | 50k | full Persian Wikipedia dump | — |
| [`fa_floret_wiki_200k`](https://huggingface.co/Phazel/fa-floret-wiki-vectors) | 200k | full Persian Wikipedia dump, 5 epochs | `lg` tier |
Trained pipelines that consume these tables, plus the measured accuracy deltas each table
buys, are in [`spacy-persian`](https://github.com/Fazel94/spacy-persian) (`docs/MODELS.md`
§6-7).
## Sources and licence
| Source | Author | Licence |
| --- | --- | --- |
| [`fa_floret` static vectors, 50k rows x 300d, 400k Persian documents](https://github.com/Fazel94/spacy-persian) | Kiyarash Fazeli | CC BY-SA 4.0 |
Released under **CC BY-SA 4.0**, the same licence the `md` pipelines that embed this table
carry.
|