Skip to content

chore: Migrate gsutil usage to gcloud storage #245

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
risha-vijayvargiya-22 wants to merge 1 commit into
base: master
Choose a base branch
from
Open

chore: Migrate gsutil usage to gcloud storage #245

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion language/capwap/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -274,7 +274,7 @@ python evaluation/score_captions.py \
You can download all the pre-trained captioning models here:

```bash
gsutil cp gs://capwap/models.zip .
gcloud storage cp gs://capwap/models.zip .
unzip models.zip && rm models.zip
```

Expand Down
3 changes: 1 addition & 2 deletions language/xsp/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,7 @@ sh language/xsp/data_download.sh train_only

You must also download resources for training the models (e.g., a pre-trained BERT model). Clone the [official BERT repository](https://github.com/google-research/bert) and download the BERT-Large, uncased model. We didn't use the original BERT-Large model in our main experimental results, but performance using BERT-Large is slightly behind BERT-Large+ on the Spider development set (see Table 3 in the main paper). You can ignore the vocabulary file in the zipped directory.

Finally, for the input training vocabulary, please download the text file from [this link](https://storage.googleapis.com/xsp-files/input_bert_vocabulary.txt) or `gs://xsp-files/input_bert_vocabulary.txt` via `gsutils`. We recommend to save it in the `assets` directory for each run.
Finally, for the input training vocabulary, please download the text file from [this link](https://storage.googleapis.com/xsp-files/input_bert_vocabulary.txt) or `gs://xsp-files/input_bert_vocabulary.txt` via `gcloud storage`. We recommend to save it in the `assets` directory for each run.

### For evaluation

Expand Down Expand Up @@ -273,4 +273,3 @@ An example of running this:
```
python -m language.xsp.evaluation.filter_results dataset_predictions.txt
```