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https://github.com/percevalw/nlstruct
Natural language structuring library
https://github.com/percevalw/nlstruct
deep-learning machine-learning natural-language-processing notebook python structured-data
Last synced: 5 days ago
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Natural language structuring library
- Host: GitHub
- URL: https://github.com/percevalw/nlstruct
- Owner: percevalw
- License: mit
- Created: 2019-12-20T02:38:34.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-06-05T11:45:29.000Z (6 months ago)
- Last Synced: 2024-11-14T17:53:19.517Z (6 days ago)
- Topics: deep-learning, machine-learning, natural-language-processing, notebook, python, structured-data
- Language: Python
- Size: 543 KB
- Stars: 19
- Watchers: 5
- Forks: 8
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# NLStruct
Natural language struturing library.
Currently, it implements a nested NER model and a span classification model, but other algorithms might follow.
If you find this library useful in your research, please consider citing:
```
@phdthesis{wajsburt:tel-03624928,
TITLE = {{Extraction and normalization of simple and structured entities in medical documents}},
AUTHOR = {Wajsb{\"u}rt, Perceval},
URL = {https://hal.archives-ouvertes.fr/tel-03624928},
SCHOOL = {{Sorbonne Universit{\'e}}},
YEAR = {2021},
MONTH = Dec,
KEYWORDS = {nlp ; structure ; extraction ; normalization ; clinical ; multilingual},
TYPE = {Theses},
PDF = {https://hal.archives-ouvertes.fr/tel-03624928/file/updated_phd_thesis_PW.pdf},
HAL_ID = {tel-03624928},
HAL_VERSION = {v1},
}
```
This work was performed at [LIMICS](http://www.limics.fr/), in collaboration with [AP-HP's Clinical Data Warehouse](https://eds.aphp.fr/) and funded by the [Institute of Computing and Data Science](https://iscd.sorbonne-universite.fr/).
## Features
- processes large documents seamlessly: it automatically handles tokenization and sentence splitting.
- do not train twice: an automatic caching mechanism detects when an experiment has already been run
- stop & resume with checkpoints
- easy import and export of data
- handles nested or overlapping entities
- multi-label classification of recognized entities
- strict or relaxed multi label end to end retrieval metrcis
- pretty logging with [rich-logger](https://github.com/percevalw/rich_logger)
- heavily customizable, without config files (see [train_ner.py](https://github.com/percevalw/nlstruct/blob/nlstruct/recipes/train_ner.py))
- built on top of [transformers](https://github.com/huggingface/transformers) and [pytorch_lightning](https://github.com/PyTorchLightning/pytorch-lightning)
## Training models
### How to train a NER model
```python
from nlstruct.recipes import train_ner
model = train_ner(
dataset={
"train": "path to your train brat/standoff data",
"val": 0.05, # or path to your validation data
# "test": # and optional path to your test data
},
finetune_bert=False,
seed=42,
bert_name="camembert/camembert-base",
fasttext_file="",
gpus=0,
xp_name="my-xp",
return_model=True,
)
model.save_pretrained("model.pt")
```
### How to use it
```python
from nlstruct import load_pretrained
from nlstruct.datasets import load_from_brat, export_to_brat
ner = load_pretrained("model.pt")
ner.eval()
ner.predict({"doc_id": "doc-0", "text": "Je lui prescris du lorazepam."})
# Out:
# {'doc_id': 'doc-0',
# 'text': 'Je lui prescris du lorazepam.',
# 'entities': [{'entity_id': 0,
# 'label': ['substance'],
# 'attributes': [],
# 'fragments': [{'begin': 19,
# 'end': 28,
# 'label': 'substance',
# 'text': 'lorazepam'}],
# 'confidence': 0.9998705969553088}]}
export_to_brat(ner.predict(load_from_brat("path/to/brat/test")), filename_prefix="path/to/exported_brat")
```
### How to train a NER model followed by a span classification model
```python
from nlstruct.recipes import train_qualified_ner
model = train_qualified_ner(
dataset={
"train": "path to your train brat/standoff data",
"val": 0.05, # or path to your validation data
# "test": # and optional path to your test data
},
finetune_bert=False,
seed=42,
bert_name="camembert/camembert-base",
fasttext_file="",
gpus=0,
xp_name="my-xp",
return_model=True,
)
model.save_pretrained("model.pt")
```
## Ensembling
Easily ensemble multiple models (same architecture, different seeds):
```python
model1 = load_pretrained("model-1.pt")
model2 = load_pretrained("model-2.pt")
model3 = load_pretrained("model-3.pt")
ensemble = model1.ensemble_with([model2, model3]).cuda()
export_to_brat(ensemble.predict(load_from_brat("path/to/brat/test")), filename_prefix="path/to/exported_brat")
```
## Advanced use
Should you need to further configure the training of a model, please modify directly one
of the recipes located in the [recipes](nlstruct/recipes/) folder.
### Install
This project is still under development and subject to changes.
```bash
pip install nlstruct==0.2.0
```