retailterms
A library to find retail terms in an unstructured text using NLP.
Developed by Marcel Tino (c) 2024
Examples of How To Use the library
You can use this to alter according to your requirements
from retailterms import get_retail_entities
get_retail_entities("Footfall is lower in some stores. Shrinkage has started to increase as well")
Output of the library
Entity Name type
Footfall Entity
Shrinkage Entity
1 maintainer
Release files for retailterms 0.0.22
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| retailterms-0.0.22.tar.gz | 10.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| retailterms-0.0.22-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 20.4 kB
Release files / retailterms-0.0.22.tar.gz
| Download URL | retailterms-0.0.22.tar.gz |
|---|---|
| Size | 10.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.11.5
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Release files / retailterms-0.0.22-py3-none-any.whl
| Download URL | retailterms-0.0.22-py3-none-any.whl |
|---|---|
| Size | 10.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.11.5
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