An easy-to-use wrapper library for using Transformers in Semantic Textual Similarity Tasks.
Project description
STSTransformers : Transformer based Semantic Textual Similarity.
STSTransformers provides state-of-the-art models for Semantic Textual Similarity.
Installation
you first need to install PyTorch. Please refer to PyTorch installation page regarding the specific install command for your platform.
When PyTorch has been installed, you can install from source by cloning the repository and running:
git clone https://github.com/TharinduDR/STS-Transformers.git
cd STS-Transformers
pip install -r requirements.txt
You can find the implementations in the examples folder.
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
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 ststransformers-0.0.5.tar.gz.
File metadata
- Download URL: ststransformers-0.0.5.tar.gz
- Upload date:
- Size: 42.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ff7e7ca8eb9a30be2387a89580b80a15d7a7a2bbe5c711b564d15b4569dc8057
|
|
| MD5 |
338a8860c5849ec2a67c465231fb016b
|
|
| BLAKE2b-256 |
eda9c0c3e79c14941db5e926e287bab2b00c385c0df5227840a0cfe9de5dda08
|
File details
Details for the file ststransformers-0.0.5-py3-none-any.whl.
File metadata
- Download URL: ststransformers-0.0.5-py3-none-any.whl
- Upload date:
- Size: 63.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
bba79b92f040faec61914d65889a2ad69657eb190cba2833844e8fd34d3812a9
|
|
| MD5 |
7c1d8b80899a9b92dbed8b07b2fb8662
|
|
| BLAKE2b-256 |
9db6827b12d6d7c7441a1817603f520a04e6e7f0b6b790fa4f996dccc9e3083e
|