https://github.com/erip/feiertag
An open-source neural sequence tagging toolkit.
https://github.com/erip/feiertag
hydra ner pos-tagging pytorch sequence-labeling
Last synced: 6 months ago
JSON representation
An open-source neural sequence tagging toolkit.
- Host: GitHub
- URL: https://github.com/erip/feiertag
- Owner: erip
- License: mit
- Created: 2020-11-27T23:05:52.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-12-06T14:49:05.000Z (almost 5 years ago)
- Last Synced: 2025-02-12T12:15:39.406Z (8 months ago)
- Topics: hydra, ner, pos-tagging, pytorch, sequence-labeling
- Language: Python
- Homepage:
- Size: 39.1 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Feiertag
Feiertag is an open-source neural sequence tagging toolkit built with PyTorch.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install feiertag.
```bash
git clone git@github.com:erip/feiertag.git
cd feiertag
pip install -e .
```
## Usage
Feiertag supports a handful of baseline architectures and datasets with configuration handled by [Hydra](http://hydra.cc/). An example to train an NER model on CoNLL 2003 formatted data is shown below:
```sh
feiertag-train data.train="$DATA_DIR/train.txt" data.valid="$DATA_DIR/valid.txt" data_format=conll2003 trainer.max_epochs=25 embedding.path="$EMBEDDING_DIR/glove.6B.50d.txt" model=bilstm_crf data.loader.batch_size=128 embedding.freeze=false
```
Model training is handled by [PyTorch-Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) and kwargs passed to the `pytorch_lightning.Trainer` class can be provided by Hydra override syntax. For example, to emulate `pl.Trainer(gpus='0,1', deterministic=True)`, use the following syntax:
```sh
feiertag-train ... +trainer.gpus="0,1" +trainer.deterministic=true
```
## Managing Experiments
By default, Hydra maintains an output directory structure to separate runs. Similarly, PyTorch-Lightning logs checkpoints, hyperparameters, and [Tensorboard](https://www.tensorflow.org/tensorboard/) logfiles with the following structure:
```tree
outputs
└── 2020-12-02
└── 18-02-11
├── lightning_logs
│ └── version_0
│ ├── checkpoints
│ │ └── epoch=1.ckpt
│ ├── events.out.tfevents.1606950132.erip.16601.0
│ └── hparams.yaml
└── train.log
```
To view the above experiment in tensorboard, issue `tensorboard --logdir outputs/2020-12-02/18-02-11/lightning_logs/version_0/`.
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
## License
[MIT](https://choosealicense.com/licenses/mit/)