Instructions to use jcorrie/en_extract_defined_terms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use jcorrie/en_extract_defined_terms with spaCy:
!pip install https://huggingface.co/jcorrie/en_extract_defined_terms/resolve/main/en_extract_defined_terms-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_extract_defined_terms") # Importing as module. import en_extract_defined_terms nlp = en_extract_defined_terms.load() - Notebooks
- Google Colab
- Kaggle
File size: 1,036 Bytes
1abc0d1 64766f0 1abc0d1 c55288c 1abc0d1 bff790d 1abc0d1 bcf14ef 3bb5387 1abc0d1 d2f5ed1 1abc0d1 3bb5387 a671a4b d2f5ed1 3bb5387 a671a4b 1abc0d1 a671a4b 1abc0d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | {
"lang":"en",
"name":"extract_defined_terms",
"version":"0.0.1",
"description":"",
"author":"",
"email":"",
"url":"",
"license":"",
"spacy_version":">=3.7.5,<3.8.0",
"spacy_git_version":"a6d0fc360",
"vectors":{
"width":300,
"vectors":514157,
"keys":514157,
"name":"en_vectors"
},
"labels":{
"tok2vec":[
],
"ner":[
"DEFINED_TERM",
"DOCUMENT",
"ENTITY"
]
},
"pipeline":[
"tok2vec",
"ner"
],
"components":[
"tok2vec",
"ner"
],
"disabled":[
],
"performance":{
"ents_f":0.9911504425,
"ents_p":0.9911504425,
"ents_r":0.9911504425,
"ents_per_type":{
"DOCUMENT":{
"p":1.0,
"r":0.95,
"f":0.9743589744
},
"DEFINED_TERM":{
"p":1.0,
"r":1.0,
"f":1.0
},
"ENTITY":{
"p":0.92,
"r":0.9583333333,
"f":0.9387755102
}
},
"tok2vec_loss":101.4646399242,
"ner_loss":241.5364215733
},
"requirements":[
]
} |