Loading Readme.md +4 −2 Changes for Readme.md: 4 added lines, 2 removed lines. Original line number Diff line number Diff line Loading @@ -21,7 +21,7 @@ cd src python create_data_dicts.py ``` Now we train a toy model (which will be bad because of the small data) 3. Now we train a toy model (which will be bad because of the small data) ``` cd src Loading @@ -36,7 +36,7 @@ training dev and testing data are separated with space. Given: File with (automatic) parses separated by single line, each AMR must have a \# snt:: line which is the orig. sentence of the corresponding parse. ### Prepare data for prediction 1. Prepare data for prediction ``` cd src Loading @@ -50,6 +50,8 @@ cd src python create_data_dict_for_prediction.py ../data/test/exampledata-gpla.txt gpla-parses.json ``` 2. Predict A pretrained model can be found in src/models, now we can predict the accuracies ``` Loading Loading
Readme.md +4 −2 Changes for Readme.md: 4 added lines, 2 removed lines. Original line number Diff line number Diff line Loading @@ -21,7 +21,7 @@ cd src python create_data_dicts.py ``` Now we train a toy model (which will be bad because of the small data) 3. Now we train a toy model (which will be bad because of the small data) ``` cd src Loading @@ -36,7 +36,7 @@ training dev and testing data are separated with space. Given: File with (automatic) parses separated by single line, each AMR must have a \# snt:: line which is the orig. sentence of the corresponding parse. ### Prepare data for prediction 1. Prepare data for prediction ``` cd src Loading @@ -50,6 +50,8 @@ cd src python create_data_dict_for_prediction.py ../data/test/exampledata-gpla.txt gpla-parses.json ``` 2. Predict A pretrained model can be found in src/models, now we can predict the accuracies ``` Loading
