The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to number in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, 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.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Ganjoor Poem Embeddings
Vector embeddings for 129,414 classical Persian poems from Ganjoor, one 1024-dimensional vector per poem, generated from each poem's AI-written summary using Qwen/Qwen3-Embedding-0.6B.
Built to power semantic ("find a poem about...") search — comparing a query's embedding against these lets you find poems by meaning rather than exact keyword match, e.g. a query like "a poem about the world's unfaithfulness" surfacing thematically relevant ghazals that never contain those exact words.
Code, tests, and full documentation: github.com/ganjoor/ganjoor-embeddings — this dataset card is a summary; the GitHub repo has the actual generation/verification tooling and the detailed docs referenced below.
Dataset structure
Two files, and they only make sense together:
embeddings.f32— raw binary, 129,414 rows × 1024 float32 columns, row-major, no header. 530,079,744 bytes exactly.embeddings-index.json—ids[i]gives the Ganjoor poem id for rowiofembeddings.f32, plus metadata (model, dimension, pooling method, generation timestamp).
Vectors are L2-normalized — cosine similarity between any two rows is just their dot product.
To resolve a poem id to its actual title/text/URL, look it up against ganjoor-data, Ganjoor's own public data export — these embeddings don't duplicate poem content, only reference it by id.
Source data
Generated from the PoemSummary field already present on ~95.6% of poems in ganjoor-data
(itself AI-generated, not something this project produced). See
docs/DATA_GENERATION.md
in the code repo for the full account of coverage, reproducibility, and exactly what "reproducible"
does and doesn't mean here.
Model & conventions
- Model:
Qwen/Qwen3-Embedding-0.6B, ONNX export (onnx-community/Qwen3-Embedding-0.6B-ONNX), int8-quantized variant. Apache 2.0. - Pooling: last non-padding token's hidden state (this is a decoder-style model — NOT mean pooling).
- A trailing end-of-sequence token is part of every embedded sequence — omitting it when
embedding a new query produces a genuinely different (wrong) embedding, not just a slightly
different one. See
docs/MODEL.mdfor the full list of conventions anything consuming this data must match.
Verification
Every check worth running before trusting a copy of this data — file size, duplicate ids,
NaN/Inf values, normalization, and a real semantic sanity check — is in
scripts/verify_embeddings.py
in the code repo, along with the harder-won lessons in
docs/VERIFICATION.md
for anyone building a new consumer of this data in a different language/runtime.
License
GPLv3, matching GanjoorService and
ganjoor-embeddings — a deliberate choice to
keep the whole project family under one consistent license rather than split code and data under
different terms.
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