A small example package
Project description
optastic
Using Optuna to quantify uncertainty of pre-trained models.
Project details
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
optastic-0.0.1.tar.gz
(14.3 kB
view details)
Built Distribution
optastic-0.0.1-py3-none-any.whl
(14.6 kB
view details)
File details
Details for the file optastic-0.0.1.tar.gz
.
File metadata
- Download URL: optastic-0.0.1.tar.gz
- Upload date:
- Size: 14.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3a581b3cf5103979dd0b57bad0b59a62feb5c8b6b000a3c4894cff057ea0e21a |
|
MD5 | 0b077c026db8730dcc585bce20bc6c65 |
|
BLAKE2b-256 | 043d4f62eedb46acad0a630db0fad4e56a8db48d67ed7e502ede57836836c3f7 |
File details
Details for the file optastic-0.0.1-py3-none-any.whl
.
File metadata
- Download URL: optastic-0.0.1-py3-none-any.whl
- Upload date:
- Size: 14.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 29fef376bc81f755c785fef15c48ba084001712212f33c56aa7d31de96281712 |
|
MD5 | 28049d23e1614d139c1ac7cdd14690d9 |
|
BLAKE2b-256 | 35e681057af07f315bccbcd4f651c297d4ae10ec5e98e1c4bb55da5c99ccc9d1 |