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entity-embed

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0.0.6 entity_embed-0.0.6-py2.py3-none-any.whl

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Project: entity-embed
Version: 0.0.6
Filename: entity_embed-0.0.6-py2.py3-none-any.whl
Download: [link]
Size: 35986
MD5: daf40601fc2eb1923ab06064842fdb70
SHA256: 056b52ee1067debb275f8155c0d6fb63343822dfbaf0623b35f9dd93435a7330
Uploaded: 2021-07-16 17:07:25 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: entity-embed
Version: 0.0.6
Summary: Transform entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.
Author: Flávio Juvenal (Vinta Software)
Author-Email: flavio[at]vinta.com.br
Home-Page: https://github.com/vintasoftware/entity-embed
License: MIT license
Keywords: record linkage,entity resolution,deduplication,embedding
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Requires-Dist: click (<8.0,==7.1.2)
Requires-Dist: more-itertools (<9.0,>=8.6.0)
Requires-Dist: n2 (<1.2,>=0.1.7)
Requires-Dist: numpy (>=1.19.0)
Requires-Dist: ordered-set (>=4.0.2)
Requires-Dist: pytorch-lightning (<1.3,>=1.1.6)
Requires-Dist: pytorch-metric-learning (<1.0,>=0.9.98)
Requires-Dist: regex (>=2020.11.13)
Requires-Dist: torch (<1.9,>=1.7.1)
Requires-Dist: torchtext (<0.10,>=0.8)
Requires-Dist: torchvision (>=0.8.2<0.10)
Requires-Dist: tqdm (>=4.53.0)
Description-Content-Type: text/markdown
[Description omitted; length: 6146 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.36.2)
Root-Is-Purelib: true
Tag: py2-none-any
Tag: py3-none-any

RECORD

Path Digest Size
entity_embed/__init__.py sha256=HC6fCj26RciouornSKkr3B4WFzD2VVC_crzYx6cxKB0 714
entity_embed/cli.py sha256=B6Q564ZoFAktuMrODFF6vuVVGFH5W0zaKzdw1G8N_PM 17828
entity_embed/data_modules.py sha256=pLYNpNofD8uhDW-fgOZEAEHKu4x0sTWGQxMiBJp12Ao 11296
entity_embed/early_stopping.py sha256=Apnlgk5K_rL9SMTBrdCivqqLU47gwlHyJY14Id-EQ0Q 1671
entity_embed/entity_embed.py sha256=--WQ-gwW5xj4XHftR8CPwpp6FQX0mJc17737Cfuaov0 21268
entity_embed/evaluation.py sha256=GDSICOiweesPUsu1yUrwAkjIR8_WrKXuRYOs2X36sm0 1813
entity_embed/helpers.py sha256=wVNHiFuJzvEbbEa3GQCK9gtLILFaSlaviFLJZ5KgQ4A 1201
entity_embed/indexes.py sha256=4R8TZNuUVhCzTumwb3AXzRA-l4hbnq7Qclbbb3JulSw 5446
entity_embed/models.py sha256=Aw1zUv-IodaRXjYJnTi-GYOi5KbueZf27ycRd0h_UDk 11543
entity_embed/benchmarks/__init__.py sha256=Myh-dJSiSwJnpOzX-h_nQtWA4xKovVa5vfGop7c41R8 603
entity_embed/benchmarks/abt_buy.py sha256=6JGn-6tt-CJ7Cj7xgZvHMbuXR7Dd56Sux-l48mFfe8s 345
entity_embed/benchmarks/amazon_google.py sha256=cbEvparbgG-j7hWIycGKczIrtlO7sFwWuI1II6PYeQM 417
entity_embed/benchmarks/base.py sha256=WVENOW6ck2blLj16TbsMFs-AtQ6db47SgnV9nW6SDdY 4665
entity_embed/benchmarks/beer.py sha256=Oypd5Jlxjwy5Tm9AH1obwr8vpsnb1XyIiZLtjr8teVY 337
entity_embed/benchmarks/company.py sha256=2MvjPNCNI0j9A02PT63nfkf3PHP9y1eT_wpTNW93qt8 346
entity_embed/benchmarks/dblp_acm_structured.py sha256=DNtBEFPsfn-K_a_IA-lX3Ddok7CwBa8FdsEWZ_KCe_Y 375
entity_embed/benchmarks/dblp_scholar_structured.py sha256=4QFmlhMwCP01_0_J9_57zEmubPMFCEdwhFr0UGrRqcY 411
entity_embed/benchmarks/fodors_zagats.py sha256=5w-q9B4cd2U7m4DkQwoGij7AFCAFltXCnnlqJQh5jmw 387
entity_embed/benchmarks/itunes_amazon_structured.py sha256=ht882Q76TdmmPHVJrgO9khExuAi_medmOZCet0oZydc 409
entity_embed/benchmarks/walmart_amazon_structured.py sha256=B7Fd_de5MGMyE_CIr4h8lztyr9bUN0YaCdv601Bw2_w 413
entity_embed/data_utils/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
entity_embed/data_utils/datasets.py sha256=RxSoq04btUIERseiGZ-1Sl5lqnfXBpzzpMwWQKJZeTo 5021
entity_embed/data_utils/field_config_parser.py sha256=4I00r8SUQ_O0dd1fRBPrbb2UbDdcb2cB1UVJ4n_Bw94 6296
entity_embed/data_utils/numericalizer.py sha256=PiYdhDvNjoVnafw8adoyBtVPxq6bco74efdxFqr0pY4 5484
entity_embed/data_utils/union_find.py sha256=MRX-FobsFobAEU5jaDgNiLoJ6GII6FJJ6Uda4ARwbuU 1152
entity_embed/data_utils/utils.py sha256=jw_nc5mZFKpCBVgbIdpmJ6yPJsL33sV-cS6k10Njss4 8574
entity_embed-0.0.6.dist-info/AUTHORS.rst sha256=qH-2XrIqhKjP1aguRtzYWH2x1-DGWrSRQClv1eSpBvE 342
entity_embed-0.0.6.dist-info/LICENSE sha256=QRlHbKhqtMeTSLT4DBOHTogzFLQSGdm_70loWIGDvKI 1107
entity_embed-0.0.6.dist-info/METADATA sha256=d2jAT8QH0FOTTX2dHjIFetkPulw_hVm2o_yUeluWx1c 7574
entity_embed-0.0.6.dist-info/WHEEL sha256=Z-nyYpwrcSqxfdux5Mbn_DQ525iP7J2DG3JgGvOYyTQ 110
entity_embed-0.0.6.dist-info/entry_points.txt sha256=PULvI2oYbqJl4rN05VL9mG3lGhjr9-f7JgJkkyAC_gg 111
entity_embed-0.0.6.dist-info/top_level.txt sha256=1fdfgxLcVKkrXCnT872gXK5eYPNLOP7kCLq-LXwzotc 13
entity_embed-0.0.6.dist-info/RECORD — —

top_level.txt

entity_embed

entry_points.txt

[console_scripts]
entity_embed_predict = entity_embed.cli:predict
entity_embed_train = entity_embed.cli:train