# fast_gliner: Python bindings for [gline-rs](https://github.com/fbilhaut/gline-rs) (Inference Engine for GLiNER Models, written in Rust)
## ⏳ Installation
```bash
$ pip install fast_gliner
```
## 🚀 Quickstart
### Named Entity Recognition
```python
from fast_gliner import FastGLiNER
model = FastGLiNER.from_pretrained(
model_id="onnx-community/gliner_multi-v2.1",
onnx_path="onnx/model.onnx"
)
model.predict_entities("I am James Bond", ["person"])
```
Output:
```
[
{
'text': 'James Bond',
'label': 'person',
'score': 0.9012733697891235,
'start': 5,
'end': 15
}
]
```
### Relation Extraction
```python
from fast_gliner import FastGLiNER
model = FastGLiNER.from_pretrained(
model_id="onnx-community/gliner-multitask-large-v0.5",
onnx_path="onnx/model.onnx"
)
text = "Bill Gates is the founder of Microsoft."
labels = ["person", "organization"]
schema = [
{
"relation": "founder",
"subject_labels": ["person"],
"object_labels": ["organization"]
}
]
results = model.extract_relations(text, labels, schema)
from pprint import pprint
pprint(results)
```
## Development
Set up environment
```sh
$ cd fast_gliner/bindings/python
$ make dev
```
Run code formatting
```sh
$ make style
```
Release package to PyPI
```sh
$ make
$ make release
```
## References
[1] [GLiNER](https://github.com/urchade/GLiNER): Generalist Model for Named Entity Recognition using Bidirectional Transformer.
```
@inproceedings{zaratiana-etal-2024-gliner,
title = "{GL}i{NER}: Generalist Model for Named Entity Recognition using Bidirectional Transformer",
author = "Zaratiana, Urchade and
Tomeh, Nadi and
Holat, Pierre and
Charnois, Thierry",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.300",
doi = "10.18653/v1/2024.naacl-long.300",
pages = "5364--5376",
abstract = "Named Entity Recognition (NER) is essential in various Natural Language Processing (NLP) applications. Traditional NER models are effective but limited to a set of predefined entity types. In contrast, Large Language Models (LLMs) can extract arbitrary entities through natural language instructions, offering greater flexibility. However, their size and cost, particularly for those accessed via APIs like ChatGPT, make them impractical in resource-limited scenarios. In this paper, we introduce a compact NER model trained to identify any type of entity. Leveraging a bidirectional transformer encoder, our model, GLiNER, facilitates parallel entity extraction, an advantage over the slow sequential token generation of LLMs. Through comprehensive testing, GLiNER demonstrate strong performance, outperforming both ChatGPT and fine-tuned LLMs in zero-shot evaluations on various NER benchmarks.",
}
```
Raw data
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