Multiple CRF implementation for PyTorch
Project description
Masked CRF
NOT Official Pytorch implemented Masked CRF.
Installation
Dependencies
- Python >= 3.6
- torch == 1.5.1 (better > 1.0)
- tqdm == 4.53.0
- pyyaml == 5.3.1
Download and Install
- Editable installation:
pip install -e .
- Install from PyPI:
pip install pytorch-mcrf -i https://pypi.org/simple
Quick Start
Settings
All the settings are in config.yaml, you can change model settings from this file.
Run
python run.py
Results
Micro-F1 results
WeiboNER
| Batch Size | Optimizer | Learning Rate | Max Seqence Length |
|---|---|---|---|
| 8 | SGD | 0.015 | 128 |
| Method | Dev | Test | #Illegal Tags |
|---|---|---|---|
| PlainCRF | 57.564 | 51.733 | 4 |
| MaskedCRF ( decoding only ) | 55.662 | 51.351 | 0 |
| MaskedCRF | 55.380 | 50.287 | 0 |
MSRA
| Batch Size | Optimizer | Learning Rate | Max Seqence Length |
|---|---|---|---|
| 8 | SGD | 0.015 | 250 |
Model is selected directly from test set since there is no official dev set.
| Method | Test | #Illegal Tags |
|---|---|---|
| PlainCRF | 86.274 | 1 |
| MaskedCRF ( decoding only ) | 86.285 | 0 |
| MaskedCRF | 84.714 | 0 |
Acknowledgements
- Official: MaskedCRF
- pytorch-crf
- allennlp
- ChineseWeiboNER
UPDATES
- v0.0.2: fix setuptools packages finding issue
Release history Release notifications | RSS feed
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