# bulk-ner 0.25.1
[](https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb)
[](https://x.com/nicolayr_/status/1842300499011260827)
[](https://pypistats.org/packages/bulk-ner)
<p align="center">
<img src="logo.png"/>
</p>
A no-strings inference implementation framework [Named Entity Recognition (NER)](https://en.wikipedia.org/wiki/Named-entity_recognition) service of wrapped AI models.
The key features of this framework are:
1. ☑️ Native support of batching;
2. ☑️ Native long-input contexts handling.
# Installation
From PyPI:
```bash
pip install bulk-ner
```
or latest from Github:
```bash
pip install git+https://github.com/nicolay-r/bulk-ner@main
```
# Usage
## API
Please take a look at the [**related Wiki page**](https://github.com/nicolay-r/bulk-ner/wiki)
## Shell
> **NOTE:** You have to install `source-iter` package
This is an example for using `DeepPavlov==1.3.0` as an adapter for NER models passed via `--adapter` parameter:
```bash
python -m bulk_ner.annotate \
--src "test/data/test.tsv" \
--prompt "{text}" \
--batch-size 10 \
--adapter "dynamic:models/dp_130.py:DeepPavlovNER" \
--output "test-annotated.jsonl" \
%%m \
--model "ner_ontonotes_bert_mult"
```
You can choose the other models via `--model` parameter.
List of the supported models is available here:
https://docs.deeppavlov.ai/en/master/features/models/NER.html
## Deploy your model
> **Quick example**: Check out the [default DeepPavlov wrapper implementation](/models/dp_130.py)
All you have to do is to implement the `BaseNER` class that has the following protected method:
* `_forward(sequences)` -- expected to return two lists of the same length:
* `terms` -- related to the list of atomic elements of the text (usually words)
* `labels` -- B-I-O labels for each term.
## Powered by
The pipeline construction components were taken from AREkit [[github]](https://github.com/nicolay-r/AREkit)
<p float="left">
<a href="https://github.com/nicolay-r/AREkit"><img src="https://github.com/nicolay-r/ARElight/assets/14871187/01232f7a-970f-416c-b7a4-1cda48506afe"/></a>
</p>
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"description": "# bulk-ner 0.25.1 \n\n[](https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb)\n[](https://x.com/nicolayr_/status/1842300499011260827)\n[](https://pypistats.org/packages/bulk-ner)\n\n<p align=\"center\">\n <img src=\"logo.png\"/>\n</p>\n\nA no-strings inference implementation framework [Named Entity Recognition (NER)](https://en.wikipedia.org/wiki/Named-entity_recognition) service of wrapped AI models.\n\nThe key features of this framework are:\n1. \u2611\ufe0f Native support of batching;\n2. \u2611\ufe0f Native long-input contexts handling.\n\n# Installation\n\nFrom PyPI:\n```bash\npip install bulk-ner\n```\n\nor latest from Github:\n```bash\npip install git+https://github.com/nicolay-r/bulk-ner@main\n```\n\n# Usage\n\n## API\n\nPlease take a look at the [**related Wiki page**](https://github.com/nicolay-r/bulk-ner/wiki)\n\n## Shell\n\n> **NOTE:** You have to install `source-iter` package\n\nThis is an example for using `DeepPavlov==1.3.0` as an adapter for NER models passed via `--adapter` parameter:\n\n```bash\npython -m bulk_ner.annotate \\\n --src \"test/data/test.tsv\" \\\n --prompt \"{text}\" \\\n --batch-size 10 \\\n --adapter \"dynamic:models/dp_130.py:DeepPavlovNER\" \\\n --output \"test-annotated.jsonl\" \\\n %%m \\\n --model \"ner_ontonotes_bert_mult\"\n```\n\nYou can choose the other models via `--model` parameter.\n\nList of the supported models is available here: \nhttps://docs.deeppavlov.ai/en/master/features/models/NER.html\n\n## Deploy your model\n\n> **Quick example**: Check out the [default DeepPavlov wrapper implementation](/models/dp_130.py)\n\nAll you have to do is to implement the `BaseNER` class that has the following protected method:\n* `_forward(sequences)` -- expected to return two lists of the same length:\n * `terms` -- related to the list of atomic elements of the text (usually words)\n * `labels` -- B-I-O labels for each term.\n \n\n## Powered by\n\nThe pipeline construction components were taken from AREkit [[github]](https://github.com/nicolay-r/AREkit)\n\n<p float=\"left\">\n<a href=\"https://github.com/nicolay-r/AREkit\"><img src=\"https://github.com/nicolay-r/ARElight/assets/14871187/01232f7a-970f-416c-b7a4-1cda48506afe\"/></a>\n</p>\n",
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