## arekit-ss 0.24.0
[](https://colab.research.google.com/github/nicolay-r/arekit-ss/blob/master/arekit_ss.ipynb)
<p align="center">
<img src="logo.png"/>
</p>
`arekit-ss` [AREkit double "s"] -- is an object-pair context sampler
for [datasources](https://github.com/nicolay-r/AREkit/wiki/Binded-Sources),
powered by [AREkit](https://github.com/nicolay-r/AREkit)
> **NOTE:** For custom text sampling, please follow the [ARElight](https://github.com/nicolay-r/ARElight) project.
## Installation
Install dependencies:
```bash
pip install git+https://github.com/nicolay-r/arekit-ss.git@0.24.0
```
Download AREkit related data, from which `sources` are required:
```bash
python -m arekit.download_data
```
## Usage
[](https://colab.research.google.com/github/nicolay-r/arekit-ss/blob/master/arekit_ss.ipynb)
Example of composing prompts:
```bash
python -m arekit_ss.sample --writer csv --source rusentrel --sampler prompt \
--prompt "For text: '{text}', the attitude between '{s_val}' and '{t_val}' is: '{label_val}'" \
--dest_lang en --docs_limit 1
```
> **Mind the case (issue [#18](https://github.com/nicolay-r/arekit-ss/issues/18)):**
> switching to another language may affect on amount of extracted data because of `terms_per_context`
> parameter that crops context by fixed and predefined amount of words.
<details>
<summary>
## Parameters
</summary>
* `source` -- source name from the list of the [supported sources](https://github.com/nicolay-r/arekit-ss/blob/master/arekit_ss/sources/src_list.py).
* `terms_per_context` -- amount of words (terms) in between SOURCE and TARGET objects.
* `object-source-types` -- filter specific source object types
* `object-target-types` -- filter specific target object types
* `relation_types` -- list of types, in which items separated with `|` char; all by default
* `splits` -- Manual selection of the data-types related splits that should be chosen for the sampling process;
types should be separated by ':' sign; for example: 'train:test'
* `sampler` -- List of the supported samplers:
* `nn` -- CNN/LSTM architecture related, including frames annotation from [RuSentiFrames](https://github.com/nicolay-r/RuSentiFrames).
* `no-vectorize` -- flag is applicable only for `nn`, and denotes no need to generate embeddings for features
* `bert` -- BERT-based, single-input sequence.
* `prompt` -- prompt-based sampler for LLM systems [[prompt engeneering guide]](https://github.com/dair-ai/Prompt-Engineering-Guide)
* `prompt` -- text of the prompt which includes the following parameters:
* `{text}` is an original text of the sample
* `{s_val}` and `{t_val}` values of the source and target of the pairs respectively
* `{label_val}` value of the label
* `writer` -- the output format of samples:
* `csv` -- for [AREnets](https://github.com/nicolay-r/AREnets) framework;
* `jsonl` -- for [OpenNRE](https://github.com/thunlp/OpenNRE) framework.
* `sqlite` -- SQLite-3.0 database.
* `mask_entities` -- mask entity mode.
* Text translation parameters:
* `src_lang` -- original language of the text.
* `dest_lang` -- target language of the text.
* `output_dir` -- target directory for samples storing
* Limiting the amount of documents from source:
* `docs_limit` -- amount of documents to be considered for sampling from the whole source.
* `doc_ids` -- list of the document IDs.
</details>
## Powered by
* [AREkit framework](https://github.com/nicolay-r/AREkit)
Raw data
{
"_id": null,
"home_page": "https://github.com/nicolay-r/arekit-ss",
"name": "arekit-ss",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "relation extraction,data processing",
"author": "Nicolay Rusnachenko",
"author_email": "rusnicolay@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/18/e4/64461a438268d63a10cf64218475cf985c6aebf0b6d9d86243ee2bca5b9c/arekit_ss-0.24.0.tar.gz",
"platform": null,
"description": "## arekit-ss 0.24.0\n\n\n\n[](https://colab.research.google.com/github/nicolay-r/arekit-ss/blob/master/arekit_ss.ipynb)\n\n<p align=\"center\">\n <img src=\"logo.png\"/>\n</p>\n\n`arekit-ss` [AREkit double \"s\"] -- is an object-pair context sampler \nfor [datasources](https://github.com/nicolay-r/AREkit/wiki/Binded-Sources), \npowered by [AREkit](https://github.com/nicolay-r/AREkit)\n\n> **NOTE:** For custom text sampling, please follow the [ARElight](https://github.com/nicolay-r/ARElight) project.\n\n## Installation\n\nInstall dependencies:\n```bash\npip install git+https://github.com/nicolay-r/arekit-ss.git@0.24.0\n```\n\nDownload AREkit related data, from which `sources` are required:\n```bash\npython -m arekit.download_data\n```\n\n## Usage\n[](https://colab.research.google.com/github/nicolay-r/arekit-ss/blob/master/arekit_ss.ipynb)\n\nExample of composing prompts:\n```bash\npython -m arekit_ss.sample --writer csv --source rusentrel --sampler prompt \\\n --prompt \"For text: '{text}', the attitude between '{s_val}' and '{t_val}' is: '{label_val}'\" \\\n --dest_lang en --docs_limit 1\n```\n\n> **Mind the case (issue [#18](https://github.com/nicolay-r/arekit-ss/issues/18)):**\n> switching to another language may affect on amount of extracted data because of `terms_per_context`\n> parameter that crops context by fixed and predefined amount of words.\n\n<details>\n<summary>\n\n## Parameters\n</summary>\n\n* `source` -- source name from the list of the [supported sources](https://github.com/nicolay-r/arekit-ss/blob/master/arekit_ss/sources/src_list.py).\n * `terms_per_context` -- amount of words (terms) in between SOURCE and TARGET objects.\n * `object-source-types` -- filter specific source object types\n * `object-target-types` -- filter specific target object types\n * `relation_types` -- list of types, in which items separated with `|` char; all by default\n * `splits` -- Manual selection of the data-types related splits that should be chosen for the sampling process; \n types should be separated by ':' sign; for example: 'train:test'\n* `sampler` -- List of the supported samplers:\n * `nn` -- CNN/LSTM architecture related, including frames annotation from [RuSentiFrames](https://github.com/nicolay-r/RuSentiFrames).\n * `no-vectorize` -- flag is applicable only for `nn`, and denotes no need to generate embeddings for features\n * `bert` -- BERT-based, single-input sequence.\n * `prompt` -- prompt-based sampler for LLM systems [[prompt engeneering guide]](https://github.com/dair-ai/Prompt-Engineering-Guide)\n * `prompt` -- text of the prompt which includes the following parameters:\n * `{text}` is an original text of the sample\n * `{s_val}` and `{t_val}` values of the source and target of the pairs respectively\n * `{label_val}` value of the label\n* `writer` -- the output format of samples:\n * `csv` -- for [AREnets](https://github.com/nicolay-r/AREnets) framework;\n * `jsonl` -- for [OpenNRE](https://github.com/thunlp/OpenNRE) framework.\n * `sqlite` -- SQLite-3.0 database.\n* `mask_entities` -- mask entity mode.\n* Text translation parameters:\n * `src_lang` -- original language of the text.\n * `dest_lang` -- target language of the text.\n* `output_dir` -- target directory for samples storing\n* Limiting the amount of documents from source:\n * `docs_limit` -- amount of documents to be considered for sampling from the whole source.\n * `doc_ids` -- list of the document IDs.\n</details>\n\n\n\n## Powered by\n\n* [AREkit framework](https://github.com/nicolay-r/AREkit)\n\n\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "Low Resource Context Relation Sampler for contexts with relations for fact-checking and fine-tuning your LLM models, powered by AREkit",
"version": "0.24.0",
"project_urls": {
"Homepage": "https://github.com/nicolay-r/arekit-ss"
},
"split_keywords": [
"relation extraction",
"data processing"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e10d8da3e2918b8ad133acba45f6f9559e733338217a060ac1c75696bebfc4f1",
"md5": "1c50964b1b60971fe4503aaeeb6c622a",
"sha256": "8a6f85917dc474dc660115bc59d095a7c52bc4d4182f6906a8a053e6054f7896"
},
"downloads": -1,
"filename": "arekit_ss-0.24.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "1c50964b1b60971fe4503aaeeb6c622a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 23908,
"upload_time": "2023-11-07T12:26:22",
"upload_time_iso_8601": "2023-11-07T12:26:22.362894Z",
"url": "https://files.pythonhosted.org/packages/e1/0d/8da3e2918b8ad133acba45f6f9559e733338217a060ac1c75696bebfc4f1/arekit_ss-0.24.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "18e464461a438268d63a10cf64218475cf985c6aebf0b6d9d86243ee2bca5b9c",
"md5": "c571b355f82113af19805e081bc8c433",
"sha256": "cf37f76d1fd2936cb7a83278b0d944760317888c63d48d80d6f7aa914045f614"
},
"downloads": -1,
"filename": "arekit_ss-0.24.0.tar.gz",
"has_sig": false,
"md5_digest": "c571b355f82113af19805e081bc8c433",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 17881,
"upload_time": "2023-11-07T12:26:24",
"upload_time_iso_8601": "2023-11-07T12:26:24.111142Z",
"url": "https://files.pythonhosted.org/packages/18/e4/64461a438268d63a10cf64218475cf985c6aebf0b6d9d86243ee2bca5b9c/arekit_ss-0.24.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-11-07 12:26:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "nicolay-r",
"github_project": "arekit-ss",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "arekit-ss"
}
JSON
