Python library designed to simplify client-side interaction with deep learning models that perform embedding calculations.
Project description
With this library, users can easily interface with APIs that provide embedding services, allowing them to quickly generate high-quality embeddings for a wide range of natural language processing (NLP) tasks. Whether you need to perform sentiment analysis, semantic similarity matching, or any other task that requires the use of embeddings, Embedding Client makes it easy to access the power of deep learning models from within your Python environment.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
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 infrabed-0.0.1.tar.gz.
File metadata
- Download URL: infrabed-0.0.1.tar.gz
- Upload date:
- Size: 1.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
743b52f6d2bb17575712cd871933f025d5376190f4a951c5957fa3a2ad5d4d6d
|
|
| MD5 |
9725fe1d1d79ea7839264423cd8f88b7
|
|
| BLAKE2b-256 |
5b7dc9277f67b0965edfd43c7ff748dac612d88400a6740271caf9fc4a00d2d8
|
File details
Details for the file infrabed-0.0.1-py3-none-any.whl.
File metadata
- Download URL: infrabed-0.0.1-py3-none-any.whl
- Upload date:
- Size: 1.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e6f69a37ecb93280d0079c0225a41065465523f4bbc7183fba71301d5a8f8407
|
|
| MD5 |
321698190e22df891021e7e5101effbe
|
|
| BLAKE2b-256 |
61989731c31ca20e874d67c600221fc6d8828a68c7318e8e9877c418cdc4be7d
|
