wassname/Castor
1
0
Fork 0
mirror of https://github.com/wassname/Castor.git synced 2026-09-11 11:51:23 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update MP-CNN Doc with Pre-Trained Models (#111)

Browse Source 
* Delete outdated troubleshooting section

* QAEvalutor bugfix

* Add instructions for pre-trained models
This commit is contained in:
Michael Tu
2018-05-25 11:51:19 -04:00
committed by Jimmy Lin
parent 494ce36575
commit ef05240819
2 changed files with 29 additions and 48 deletions
Download Patch File Download Diff File Expand all files Collapse all files
common/evaluators/qa_evaluator.py
+3 -3
View File
@@ -16,9 +16,9 @@ class QAEvaluator(Evaluator):
for batch in self.data_loader:
qids.extend(batch.id.detach().cpu().numpy())
# Select embedding
sent1, sent2, sent1_nonstatic, sent2_nonstatic = self.get_sentence_embeddings(batch)
sent1, sent2 = self.get_sentence_embeddings(batch)
output = self.model(sent1, sent2, batch.ext_feats, batch.dataset.word_to_doc_cnt, batch.sentence_1_raw, batch.sentence_2_raw, sent1_nonstatic, sent2_nonstatic)
output = self.model(sent1, sent2, batch.ext_feats, batch.dataset.word_to_doc_cnt, batch.sentence_1_raw, batch.sentence_2_raw)
test_cross_entropy_loss += F.cross_entropy(output, batch.label, size_average=False).item()
true_labels.extend(batch.label.detach().cpu().numpy())
@@ -36,4 +36,4 @@ class QAEvaluator(Evaluator):
def get_final_prediction_and_label(self, batch_predictions, batch_labels):
predictions = batch_predictions.exp()[:, 1]
return predictions, batch_labels
return predictions, batch_labels
mp_cnn/README.md
+26 -45
View File
@@ -6,46 +6,44 @@ This is a PyTorch implementation of the following paper
Please ensure you have followed instructions in the main [README](../README.md) doc before running any further commands in this doc.
## Pre-Trained Models
We have pre-trained models for SICK, TrecQA, and WikiQA in the [Castor-models](https://git.uwaterloo.ca/jimmylin/Castor-models) repository. They are trained using the commands in each of the dataset sections below and the evaluation metrics match the reported values in those sections in our environment.
| Dataset | Model file | Command to run pre-trained model |
| --------- |:--------------------------:|:----------------------------------------------------------------------------------------------------------------------:|
| SICK | mp_cnn/mpcnn.sick.model | `python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.sick.model --dataset sick --skip-training` |
| TrecQA | mp_cnn/mpcnn.trecqa.model | `python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.trecqa.model --dataset trecqa --holistic-filters 200 --skip-training` |
| WikiQA | mp_cnn/mpcnn.wikiqa.model | `python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.wikiqa.model --dataset wikiqa --holistic-filters 100 --skip-training` |
If you want to train them yourself, please read on.
## SICK Dataset
To run MP-CNN on the SICK dataset, use the following command. `--dropout 0` is for mimicking the original paper, although adding dropout can improve performance. If you have any problems running it check the Troubleshooting section below.
To run MP-CNN on the SICK dataset, use the following command. `--dropout 0` is for mimicking the original paper, although adding dropout can improve results. If you have any problems running it check the Troubleshooting section below.
```
python -m mp_cnn mpcnn.sick.model.castor --dataset sick --epochs 19 --epsilon 1e-7 --dropout 0
python -m mp_cnn mpcnn.sick.model.castor --dataset sick --epochs 19 --dropout 0 --lr 0.0005
```
| Implementation and config | Pearson's r | Spearman's p |
| -------------------------------- |:-------------:|:-------------:|
| Paper | 0.8686 | 0.8047 |
| PyTorch using above config | 0.8684 | 0.8083 |
## MSRVID Dataset
To run MP-CNN on the MSRVID dataset, use the following command:
```
python -m mp_cnn mpcnn.msrvid.model.castor --dataset msrvid --batch-size 16 --epsilon 1e-7 --epochs 32 --dropout 0 --regularization 0.0025
```
| Implementation and config | Pearson's r |
| -------------------------------- |:-------------:|
| Paper | 0.9090 |
| PyTorch using above config | 0.8911 |
| Implementation and config | Pearson's r | Spearman's p | MSE |
| -------------------------------- |:-------------:|:-------------:|:----------:|
| Paper | 0.8686 | 0.8047 | 0.2606 |
| PyTorch using above config | 0.8738 | 0.8116 | 0.2414 |
## TrecQA Dataset
To run MP-CNN on (Raw) TrecQA, you first need to run `./get_trec_eval.sh` in `utils` under the repo root while inside the `utils` directory. This will download and compile the official `trec_eval` tool used for evaluation.
Then, you can run:
To run MP-CNN on the TrecQA dataset, use the following command:
```
python -m mp_cnn mpcnn.trecqa.model --dataset trecqa --epochs 5 --regularization 0.0005 --dropout 0.5 --eps 0.1
python -m mp_cnn mpcnn.trecqa.model --dataset trecqa --epochs 5 --holistic-filters 200 --lr 0.00018 --regularization 0.0006405 --dropout 0
```
| Implementation and config | map | mrr |
| -------------------------------- |:------:|:------:|
| Paper | 0.762 | 0.830 |
| PyTorch using above config | 0.7904 | 0.8223 |
| Paper | 0.764 | 0.827 |
| PyTorch using above config | 0.777 | 0.821 |
The paper results are reported in [Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks](https://dl.acm.org/citation.cfm?id=2983872).
This are the TrecQA raw dataset results. The paper results are reported in [Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks](https://dl.acm.org/citation.cfm?id=2983872).
## WikiQA Dataset
@@ -53,40 +51,23 @@ You also need `trec_eval` for this dataset, similar to TrecQA.
Then, you can run:
```
python -m mp_cnn mpcnn.wikiqa.model --epochs 10 --dataset wikiqa --batch-size 64 --lr 0.0004 --regularization 0.02
python -m mp_cnn mpcnn.wikiqa.model --epochs 10 --dataset wikiqa --epochs 5 --holistic-filters 100 --lr 0.00042 --regularization 0.0001683 --dropout 0
```
| Implementation and config | map | mrr |
| -------------------------------- |:------:|:------:|
| Paper | 0.693 | 0.709 |
| PyTorch using above config | 0.693 | 0.7091 |
| PyTorch using above config | 0.717 | 0.729 |
The paper results are reported in [Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks](https://dl.acm.org/citation.cfm?id=2983872).
These are not the optimal hyperparameters but they are decent. This README will be updated with more optimal hyperparameters and results in the future.
To see all options available, use
```
python -m mp_cnn --help
```
## Troubleshooting
### ModuleNotFoundError: datasets
```
Traceback (most recent call last):
File "__main__.py", line 9, in <module>
from common.dataset import DatasetFactory
File "/u/z3tu/castorini/Castor/common/dataset.py", line 12, in <module>
from datasets.sick import SICK
ModuleNotFoundError: No module named 'datasets'
```
You need to make sure the repository root is in your `PYTHONPATH` environment variable. One way to do this is while you are in the repo root (Castor) as your current working directory, run `export PYTHONPATH=$(pwd)`.
## Optional Dependencies
To optionally visualize the learning curve during training, we make use of https://github.com/lanpa/tensorboard-pytorch to connect to [TensorBoard](https://github.com/tensorflow/tensorboard). These projects require TensorFlow as a dependency, so you need to install TensorFlow before running the commands below. After these are installed, just add `--tensorboard` when running `main.py` and open TensorBoard in the browser.
To optionally visualize the learning curve during training, we make use of https://github.com/lanpa/tensorboard-pytorch to connect to [TensorBoard](https://github.com/tensorflow/tensorboard). These projects require TensorFlow as a dependency, so you need to install TensorFlow before running the commands below. After these are installed, just add `--tensorboard` when running the training commands and open TensorBoard in the browser.
```sh
pip install tensorboardX