Commonly-used functions for building sequence labeling models.
Project description
SeqLbToolkit
This repo realizes Sequence Labeling Toolkits, a toolkit box containing useful functions for accelerating implementing sequences labeling deep learning models such as BiLSTM-CRF or BERT for Token Classification.
1. Installation
- Install from PyPI:
pip install -U SeqLbLoolkit
2. Documentation
Please refer to wiki for documentation.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
SeqLbToolkit-0.3.2.tar.gz
(34.6 kB
view details)
Built Distribution
File details
Details for the file SeqLbToolkit-0.3.2.tar.gz.
File metadata
- Download URL: SeqLbToolkit-0.3.2.tar.gz
- Upload date:
- Size: 34.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 15699e1807f4065ef6f60313e98cf9b29acf2f45ede3f66a0d969d5ed25637ca |
|
| MD5 | de6c481ba339e3c032ec09a52652c442 |
|
| BLAKE2b-256 | b33b780aa7cf2bb13145e37a80e407fdde4f557676dd714b8ab49b2ef9ade330 |
File details
Details for the file SeqLbToolkit-0.3.2-py3-none-any.whl.
File metadata
- Download URL: SeqLbToolkit-0.3.2-py3-none-any.whl
- Upload date:
- Size: 40.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b545edc237a1c0412b154c6a4a868347b7edc24361bc392fb86a6bd562aa74de |
|
| MD5 | 24090138e432bfc56c68a2cb54b31da7 |
|
| BLAKE2b-256 | a018d779c603a933da5b1ad154642f038eafab1a02cce11a7d2a8ac2e6d409fc |
