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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 0xbb 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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data-intermediate
If you are looking for our test ready version, please refer to mango-ttic/data
Find more about us at mango.ttic.edu
Folder Structure
Each folder inside data-intermediate
contains
all intermediate files we used during data annotation and generation. Here is the tree structure from game data-intermediate/night
.
data-intermediate/night/
├── night.all2all.json # all simple paths between any 2 nodes
├── night.all_pairs.json # all connectivity between any 2 nodes
├── night.anno2code.json # annotation to codename mapping
├── night.code2anno.json # codename to annotation mapping
├── night.edges.json # list of all edges
├── night.map.human # human map derived from human annotation
├── night.map.machine # machine map derived from exported action sequences
├── night.map.reversed # reverse map derived from human annotation map
├── night.moves # list of mentioned actions
├── night.nodes.json # list of all nodes
├── night.valid_moves.csv # human annotation
├── night.walkthrough # enriched walkthrough exported from Jericho simulator
└── night.walkthrough_acts # action sequences 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-intermediate-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-intermediate.tar.zst
: contains all steps of each walkthrough.
Word-only & Word+ID
Word-only
data-intermediate.tar.zst
: Nodes are annotated by additional descriptive text to distinguish different locations with similar names.Word + Object ID
data-intermediate-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-intermediate-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-intermediate.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-intermediate
# or, use hf-mirror if your connection to huggingface.co is slow
# git clone https://hf-mirror.com/datasets/mango-ttic/data-intermediate
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-intermediate
# 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-intermediate
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-intermediate.tar.zst
or, verbosely decompress
zstd -d -c data-intermediate.tar.zst | tar -xvf -
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