facebookresearch / treenlg Goto Github PK

I have trained a model, and attempted to evaluate using bash scripts/generate.weather.lstm.sh but get various errors since it seems this was tested in a UNIX system. My first error was readlink does not have the -f option. This was solved by using greadlink and re-running now shows the tree score but now throws new grep errors:

Tree accuracy: 99.84 (3116 / 3121)                                                                                                                                                      
usage: grep [-abcDEFGHhIiJLlmnOoqRSsUVvwxZ] [-A num] [-B num] [-C[num]]
        [-e pattern] [-f file] [--binary-files=value] [--color=when]
        [--context[=num]] [--directories=action] [--label] [--line-buffered]
        [--null] [pattern] [file ...]
usage: grep [-abcDEFGHhIiJLlmnOoqRSsUVvwxZ] [-A num] [-B num] [-C[num]]
        [-e pattern] [-f file] [--binary-files=value] [--color=when]
        [--context[=num]] [--directories=action] [--label] [--line-buffered]
        [--null] [pattern] [file ...]
EOF encountered in a comment.
Failure rate: 0.00 (0 / 0)

replacing failures from /Users/nguyen/src/npp/TreeNLG/checkpoints/weather.lstm/gen.txt
/Users/nguyen/src/npp/TreeNLG/checkpoints/weather.lstm/gen.txt does not exist```


Any advice or help solving this issue so evaluation is allowed on MacOS would be great.

Hey,
Please can you refer me some link or direction where I can get, the complete information to understand the data stracture/formatting used with data given in the repo.

In other words, how to explain each word/term/feature of a single sample of the data-set used here?

Are there any instructions on generating the bleu score for the weather task? My output for running the commands in the readme after generating is the tree scores but I don't get any .tsv in order to run the bleu score evaluation.

I am also only getting gen.constr.txt as output rather than a tsv file or the expected gen.txt. Any guidance would be great, until then I will try to find my own solution as well.

Please can you provide the prediction guideline

When I try to use below command:
bash scripts/generate.weather.lstm.sh

I get this error:

 File "/home/mrigank/miniconda3/bin/fairseq-generate", line 33, in <module>
   sys.exit(load_entry_point('fairseq', 'console_scripts', 'fairseq-generate')())
 File "/home/mrigank/code/TreeNLG/fairseq/fairseq_cli/generate.py", line 285, in cli_main
   main(args)
 File "/home/mrigank/code/TreeNLG/fairseq/fairseq_cli/generate.py", line 38, in main
   return _main(args, sys.stdout)
 File "/home/mrigank/code/TreeNLG/fairseq/fairseq_cli/generate.py", line 126, in _main
   generator = task.build_generator(models, args)
TypeError: build_generator() takes 2 positional arguments but 3 were given
Args ::  Namespace(order_constr=False, tsv='scripts/tmp/tsv')                                                                                                                                                
Number of lines:  []
Traceback (most recent call last):
 File "compute_tree_acc.py", line 31, in <module>
   correct / len(lines) * 100, correct, len(lines)
ZeroDivisionError: division by zero
Runtime error (func=(main), adr=3): Divide by zero
Failure rate: 0.00 (0 / 0)

replacing failures from /home/mrigank/code/TreeNLG/checkpoints/weather.lstm/gen.txt
/home/mrigank/code/TreeNLG/checkpoints/weather.lstm/gen.txt does not exist```

Hello,
I need to use my own dataset to train the model or run the compute_tree_acc.py, so my problem is how to make the data as a tree type?And how many the type of attributes?I know query annotation is parse automatically by rules, and can I get these rules?

As title,I can't make smaple data in seq2seq_out.tsv, can someone give me something?

Hello,
Thanks for the paper and the code.
I would like to know if its really possible to use just the data for commercial purposes.
If this means that I'd have to buy the license, how much would it cost? and how would I go about buying it?

Thanks

sequence_to_tree's comments says:
[DG_INFORM_2 supposed to ARG:CONDITION_NOT ]
=>
NLGNode("root", children={
NLGNode("[DG_INFORM_2", children={NLGNode("ARG:CONDITION_NOT")})
})

but actual result is:
NLGNode("root", children={
NLGNode("[DG_INFORM_2")
})

scenario_to_tree's comment says:
[DG_INFORM: [ARG_TASK: get_forecast , ARG_TEMP_HIGH: 33 ] ]
=>
NLGNode("root", children={
NLGNode("[1_DG_INFORM", children={
NLGNode("ARG_TEMP_HIGH", children={
NLGNode("33")
}),
NLGNode("ARG_TASK", children={
NLGNode("get_forecast")
})
})
})

but actual result is:
NLGNode("root", children={
NLGNode("[1_DG_INFORM", children={
NLGNode("ARG_TASK", children={
NLGNode("get_forecast , ARG_TEMP_HIGH: 33")
})
})
})

Thanks!

Hello,

Thanks for providing these very useful resources. Is there any chance that the users' raw queries would be available as part of the datasets (or a subset of the datasets)? I've seen that the seq2seq_out.tsv example has such questions similar to Table 6 in the paper. So I was wondering if this would be possible to include. That would be highly useful.

Thanks!
Hamza