Instructions to use valurank/en_readability with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use valurank/en_readability with spaCy:
!pip install https://huggingface.co/valurank/en_readability/resolve/main/en_readability-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_readability") # Importing as module. import en_readability nlp = en_readability.load() - Notebooks
- Google Colab
- Kaggle
A Spacy pipeline for generating readability scores
| Feature | Description |
|---|---|
| Name | en_readability |
| Version | 0.1 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | tok2vec, tagger, parser, attribute_ruler, readability |
| Components | tok2vec, tagger, parser, attribute_ruler, readability |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | Valurank |
Label Scheme
View label scheme (95 labels for 2 components)
| Component | Labels |
|---|---|
tagger |
$, '', ,, -LRB-, -RRB-, ., :, ADD, AFX, CC, CD, DT, EX, FW, HYPH, IN, JJ, JJR, JJS, LS, MD, NFP, NN, NNP, NNPS, NNS, PDT, POS, PRP, PRP$, RB, RBR, RBS, RP, SYM, TO, UH, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WP$, WRB, XX, _SP, ```` |
parser |
ROOT, acl, acomp, advcl, advmod, agent, amod, appos, attr, aux, auxpass, case, cc, ccomp, compound, conj, csubj, csubjpass, dative, dep, det, dobj, expl, intj, mark, meta, neg, nmod, npadvmod, nsubj, nsubjpass, nummod, oprd, parataxis, pcomp, pobj, poss, preconj, predet, prep, prt, punct, quantmod, relcl, xcomp |
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