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Update README.md

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@ -37,17 +37,17 @@ nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash
For example, to train a Multitask Question Answering Network (MQAN) on the Stanford Question Answering Dataset (SQuAD):
```bash
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/train.py --train_tasks squad --gpus DEVICE_ID"
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/train.py --train_tasks squad --gpu DEVICE_ID"
```
To multitask with the fully joint, round-robin training described in the paper, you can add multiple tasks:
```bash
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/train.py --train_tasks squad iwslt.en.de --train_iterations 1 --gpus DEVICE_ID"
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/train.py --train_tasks squad iwslt.en.de --train_iterations 1 --gpu DEVICE_ID"
```
To train on the entire Natural Language Decathlon:
```bash
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/train.py --train_tasks squad iwslt.en.de cnn_dailymail multinli.in.out sst srl zre woz.en wikisql schema --train_iterations 1 --gpus DEVICE_ID"
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/train.py --train_tasks squad iwslt.en.de cnn_dailymail multinli.in.out sst srl zre woz.en wikisql schema --train_iterations 1 --gpu DEVICE_ID"
```
You can find a list of commands in `experiments.sh` that correspond to each trained model that we used to report validation results comparing models and training strategies in the paper.
@ -83,12 +83,12 @@ If you are having trouble with the specified port on either machine, run `lsof -
You can evaluate a model for a specific task with `EVALUATION_TYPE` as `validation` or `test`:
```bash
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/predict.py --evaluate EVALUATION_TYPE --path PATH_TO_CHECKPOINT_DIRECTORY --gpus DEVICE_ID --tasks squad"
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/predict.py --evaluate EVALUATION_TYPE --path PATH_TO_CHECKPOINT_DIRECTORY --gpu DEVICE_ID --tasks squad"
```
or evaluate on the entire decathlon by removing any task specification:
```bash
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/predict.py --evaluate EVALUATION_TYPE --path PATH_TO_CHECKPOINT_DIRECTORY --gpus DEVICE_ID"
nvidia-docker run -it --rm -v `pwd`:/decaNLP/ -u $(id -u):$(id -g) decanlp bash -c "python /decaNLP/predict.py --evaluate EVALUATION_TYPE --path PATH_TO_CHECKPOINT_DIRECTORY --gpu DEVICE_ID"
```
For test performance, please use the original [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/), and [WikiSQL](https://github.com/salesforce/WikiSQL) evaluation systems.