mirror of
https://github.com/wassname/Castor.git
synced 2026-09-09 11:13:20 +08:00
Achyudh Ram
ed4f01852e
* Add ReutersTrainer, ReutersEvaluator options in Factory classes * Add Reuters to Kim-CNN command line arguments * Fix SST dataset path according to changes in Kim-CNN args The dataset path in args.py was made to point at the dataset folder rather than dataset/SST folder. Hence SST folder was added to paths in the SST dataset class * Add Reuters dataset class, and support in __main__ * Add Reuters dataset trainers and evaluators * Remove debug print statement in reuters_evaluator * Fix rounding bug in reuters_trainer and reuters_evaluator * Add LSTM for baseline text classification measurements * Add eval metrics for lstm_baseline * Set batch_first param in lstm_baseline * Remove onnx args from lstm_baseline
33 lines
1.1 KiB
Markdown
33 lines
1.1 KiB
Markdown
# lstm_baseline
|
|
|
|
Implementation of a standard LSTM using PyTorch and Torchtext for text classification baseline measurements.
|
|
|
|
## Model Type
|
|
|
|
- rand: All words are randomly initialized and then modified during training.
|
|
- static: A model with pre-trained vectors from [word2vec](https://code.google.com/archive/p/word2vec/). All words -- including the unknown ones that are initialized with zero -- are kept static and only the other parameters of the model are learned.
|
|
- non-static: Same as above but the pretrained vectors are fine-tuned for each task.
|
|
|
|
## Quick Start
|
|
|
|
To run the model on Reuters dataset on static, just run the following from the Castor working directory.
|
|
|
|
```
|
|
python -m lstm_baseline --mode static
|
|
```
|
|
|
|
## Dataset
|
|
|
|
We experiment the model on the following datasets.
|
|
|
|
- Reuters dataset - ModApte splits
|
|
|
|
## Settings
|
|
|
|
Adadelta is used for training.
|
|
|
|
## TODO
|
|
- Support ONNX export. Currently throws a ONNX export failed (Couldn't export Python operator forward_flattened_wrapper) exception.
|
|
- Add dataset results with different hyperparameters
|
|
- Parameters tuning
|