Pre-release
This release is a pre-release and may not be stable for production use.
DeepSim
This toolkit provides deep learning-based similarity utilities.
Example
'''models for type
BERT:
shibing624/text2vec-base-chinese
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
hfl/chinese-macbert-base:
uer/roberta-medium-wwm-chinese-cluecorpussmall
hfl/chinese-roberta-wwm-ext
Langboat/mengzi-bert-base
WMD:
w2v-light-tencent-chinese
'''
from deepsim import *
sim_utils=SimilarityUtils(type='w2v')
list_r=sim_utils.get_similarity('I like you!','I love you!')
print(list_r)
License
The deepsim project is provided by Donghua Chen.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
deepsim-0.0.1a0.tar.gz
(7.5 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file deepsim-0.0.1a0.tar.gz.
File metadata
- Download URL: deepsim-0.0.1a0.tar.gz
- Upload date:
- Size: 7.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.1 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
46ff9aa93f7475655a6f892016ec1f3fceb1d4979b9edf323d7cf47b0334080c
|
|
| MD5 |
70c69c9adb8f4b74cc6ee9f0acddc805
|
|
| BLAKE2b-256 |
d6b526e75bfc19fc2139cd754ed07a2980269eb653ef6143c5dae9b832578fe0
|
File details
Details for the file deepsim-0.0.1a0-py3-none-any.whl.
File metadata
- Download URL: deepsim-0.0.1a0-py3-none-any.whl
- Upload date:
- Size: 5.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.1 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
680437c053fbef521bc5ff4d9db781adba146f93e95452d432fd3f0e1a11b18d
|
|
| MD5 |
70cb9a82ab25e5b384f918404ea397e9
|
|
| BLAKE2b-256 |
359e420d21831e185880eed5c8e9de9d7060c3bf1fb08c853457c10878c771e5
|