<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
# floret: fastText + Bloom embeddings for compact, full-coverage vectors with spaCy
floret is an extended version of [fastText](https://fasttext.cc) that can
produce word representations for any word from a compact vector table. It
combines:
- fastText's subwords to provide embeddings for any word
- Bloom embeddings ("hashing trick") for a compact vector table
## Installation
```bash
pip install floret
```
## Usage
Train floret vectors using the options:
- `mode`: `"floret"`, storing both words and subwords in the same compact hash
table
- `hashCount`: store each entry in 1-4 rows in the hash table (recommended:
`2`)
- `bucket`: in combination with `hashCount>1`, the size of the hash table can
be greatly reduced (recommended: `25000`--`100000`, reduced from the fastText
default of `2000000`)
- `minn`: min length of char ngram (default: `3`)
- `maxn`: max length of char ngram (default: `6`)
```python
import floret
# train vectors
model = floret.train_unsupervised(
"data.txt",
model="cbow",
mode="floret",
hashCount=2,
bucket=50000,
minn=3,
maxn=6,
)
# query vector
model.get_word_vector("broccoli")
# save full model
model.save_model("vectors.bin")
# export standard word-only vector table
model.save_vectors("vectors.vec")
# export floret vector table
model.save_floret_vectors("vectors.floret")
```
**Note:** with the default setting `mode="fasttext"`, `floret` trains original
fastText vectors.
## Use floret vectors in spaCy
Import floret vectors into spaCy v3.2+:
```bash
spacy init vectors LANG vectors.floret spacy_vectors_model --mode floret
```
## Notes
`floret` contains all features of the original [`fasttext`
module](https://pypi.org/project/fasttext). See the [`fasttext`
docs](https://fasttext.cc/docs/en/python-module.html) for more information.
The `fasttext` and `floret` binary formats saved with
`model.save_model("model.bin")` are not compatible.
Raw data
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"description": "<a href=\"https://explosion.ai\"><img src=\"https://explosion.ai/assets/img/logo.svg\" width=\"125\" height=\"125\" align=\"right\" /></a>\n\n# floret: fastText + Bloom embeddings for compact, full-coverage vectors with spaCy\n\nfloret is an extended version of [fastText](https://fasttext.cc) that can\nproduce word representations for any word from a compact vector table. It\ncombines:\n\n- fastText's subwords to provide embeddings for any word\n- Bloom embeddings (\"hashing trick\") for a compact vector table\n\n## Installation\n\n```bash\npip install floret\n```\n\n## Usage\n\nTrain floret vectors using the options:\n\n- `mode`: `\"floret\"`, storing both words and subwords in the same compact hash\n table\n- `hashCount`: store each entry in 1-4 rows in the hash table (recommended:\n `2`)\n- `bucket`: in combination with `hashCount>1`, the size of the hash table can\n be greatly reduced (recommended: `25000`--`100000`, reduced from the fastText\n default of `2000000`)\n- `minn`: min length of char ngram (default: `3`)\n- `maxn`: max length of char ngram (default: `6`)\n\n```python\nimport floret\n\n# train vectors\nmodel = floret.train_unsupervised(\n \"data.txt\",\n model=\"cbow\",\n mode=\"floret\",\n hashCount=2,\n bucket=50000,\n minn=3,\n maxn=6,\n)\n\n# query vector\nmodel.get_word_vector(\"broccoli\")\n\n# save full model\nmodel.save_model(\"vectors.bin\")\n\n# export standard word-only vector table\nmodel.save_vectors(\"vectors.vec\")\n\n# export floret vector table\nmodel.save_floret_vectors(\"vectors.floret\")\n```\n\n**Note:** with the default setting `mode=\"fasttext\"`, `floret` trains original\nfastText vectors.\n\n## Use floret vectors in spaCy\n\nImport floret vectors into spaCy v3.2+:\n\n```bash\nspacy init vectors LANG vectors.floret spacy_vectors_model --mode floret\n```\n\n## Notes\n\n`floret` contains all features of the original [`fasttext`\nmodule](https://pypi.org/project/fasttext). See the [`fasttext`\ndocs](https://fasttext.cc/docs/en/python-module.html) for more information.\n\nThe `fasttext` and `floret` binary formats saved with\n`model.save_model(\"model.bin\")` are not compatible.\n",
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