Datasets:

mango-ttic
/

data

Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 160, in compute
                  compute_split_names_from_info_response(
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 132, in compute_split_names_from_info_response
                  config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config)
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 539, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.9/tarfile.py", line 190, in nti
                  s = nts(s, "ascii", "strict")
                File "/usr/local/lib/python3.9/tarfile.py", line 174, in nts
                  return s.decode(encoding, errors)
              UnicodeDecodeError: 'ascii' codec can't decode byte 0x87 in position 1: ordinal not in range(128)
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.9/tarfile.py", line 2588, in next
                  tarinfo = self.tarinfo.fromtarfile(self)
                File "/usr/local/lib/python3.9/tarfile.py", line 1292, in fromtarfile
                  obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)
                File "/usr/local/lib/python3.9/tarfile.py", line 1234, in frombuf
                  chksum = nti(buf[148:156])
                File "/usr/local/lib/python3.9/tarfile.py", line 193, in nti
                  raise InvalidHeaderError("invalid header")
              tarfile.InvalidHeaderError: invalid header
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 72, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 25, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1574, in __iter__
                  for x in self.generator(*self.args, **self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1634, in _iter_from_urlpath
                  yield from cls._iter_tar(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1585, in _iter_tar
                  stream = tarfile.open(fileobj=f, mode="r|*")
                File "/usr/local/lib/python3.9/tarfile.py", line 1822, in open
                  t = cls(name, filemode, stream, **kwargs)
                File "/usr/local/lib/python3.9/tarfile.py", line 1703, in __init__
                  self.firstmember = self.next()
                File "/usr/local/lib/python3.9/tarfile.py", line 2600, in next
                  raise ReadError(str(e))
              tarfile.ReadError: invalid header
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 76, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

data

If you are looking for our intermediate labeling version, please refer to mango-ttic/data-intermediate

Find more about us at mango.ttic.edu

Folder Structure

Each folder inside data contains the cleaned up files used during LLM inference and results evaluations. Here is the tree structure from game data/night .

data/night/
├── night.actions.json      # list of mentioned actions
├── night.all2all.jsonl      # all simple paths between any 2 locations
├── night.all_pairs.jsonl    # all connectivity between any 2 locations
├── night.edges.json        # list of all edges
├── night.locations.json    # list of all locations
└── night.walkthrough       # enriched walkthrough exported from Jericho simulator

Variations

70-step vs all-step version

In our paper, we benchmark using the first 70 steps of the walkthrough from each game. We also provide all-step versions of both data and data-intermediate collection.

70-step data-70steps.tar.zst: contains the first 70 steps of each walkthrough. If the complete walkthrough is shorter than 70 steps, then all steps are used.
All-step data.tar.zst: contains all steps of each walkthrough.

Word-only & Word+ID

Word-only data.tar.zst: Nodes are annotated by additional descriptive text to distinguish different locations with similar names.
Word + Object ID data-objid.tar.zst: variation of the word-only version, where nodes are labeled using minimaly fixed names with object id from Jericho simulator.
Word + Random ID data-randid.tar.zst: variation of the Jericho ID version, where the Jericho object id replaced with randomly generated integer.

We primarily rely on the word-only version as benchmark, yet providing word+ID version for diverse benchmark settings.

How to use

We use data.tar.zst as an example here.

1. download from Huggingface

by directly download

You can selectively download certain variation of your choice.

by git

Make sure you have git-lfs installed

git lfs install
git clone https://huggingface.co/datasets/mango-ttic/data

# or, use hf-mirror if your connection to huggingface.co is slow
# git clone https://hf-mirror.com/datasets/mango-ttic/data

If you want to clone without large files - just their pointers

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/mango-ttic/data

# or, use hf-mirror if your connection to huggingface.co is slow
# GIT_LFS_SKIP_SMUDGE=1 git clone https://hf-mirror.com/datasets/mango-ttic/data

2. decompress

Because some json files are huge, we use tar.zst to package the data efficiently.

silently decompress

tar -I 'zstd -d' -xf data.tar.zst

or, verbosely decompress

zstd -d -c data.tar.zst | tar -xvf -

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