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.
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Release files for infrabed 0.0.6
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| infrabed-0.0.6.tar.gz | 6.0 kB | Without provenance | Details for infrabed-0.0.6.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| infrabed-0.0.6-py3-none-any.whl | Python 3 | none | any | Details for infrabed-0.0.6-py3-none-any.whl |
Total release size: 12.3 kB
Release files / infrabed-0.0.6.tar.gz
| Download URL | infrabed-0.0.6.tar.gz |
|---|---|
| Size | 6.0 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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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.10
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Release files / infrabed-0.0.6-py3-none-any.whl
| Download URL | infrabed-0.0.6-py3-none-any.whl |
|---|---|
| Size | 6.3 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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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.10
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Security for infrabed 0.0.6
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