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Datasets:
Yatorou
/
ChiShenMe

Languages:
Chinese
Tags:
recipe
rag
embeddings
bge-m3
android
License:
Dataset card Files
xet
 Community
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model: string
backend: string
input: string
outDir: string
vectors: string
metadata: string
count: int64
allocatedCount: int64
dimension: int64
dtype: string
normalized: bool
denseOnly: bool
corpusPrefix: string
queryPrefix: string
maxLength: int64
batchSize: int64
fp16Inference: bool
device: string
startedAt: timestamp[s]
completedAt: timestamp[s]
android: struct<embeddingMode: string, requiresOnnxForQueryEmbedding: bool, recommendedInstallType: string>
  child 0, embeddingMode: string
  child 1, requiresOnnxForQueryEmbedding: bool
  child 2, recommendedInstallType: string
description: string
id: string
files: list<item: struct<role: string, path: string, sizeBytes: int64, sha256: string>>
  child 0, item: struct<role: string, path: string, sizeBytes: int64, sha256: string>
      child 0, role: string
      child 1, path: string
      child 2, sizeBytes: int64
      child 3, sha256: string
name: string
version: string
locale: string
license: string
embedding: struct<model: string, backend: string, dimension: int64, dtype: string, normalized: bool, denseOnly: (... 67 chars omitted)
  child 0, model: string
  child 1, backend: string
  child 2, dimension: int64
  child 3, dtype: string
  child 4, normalized: bool
  child 5, denseOnly: bool
  child 6, queryPrefix: string
  child 7, corpusPrefix: string
  child 8, maxLength: int64
schemaVersion: string
level: string
recipeCount: int64
chunkCount: int64
createdAt: timestamp[s]
to
{'schemaVersion': Value('string'), 'id': Value('string'), 'name': Value('string'), 'version': Value('string'), 'description': Value('string'), 'level': Value('string'), 'createdAt': Value('timestamp[s]'), 'locale': Value('string'), 'license': Value('string'), 'recipeCount': Value('int64'), 'chunkCount': Value('int64'), 'embedding': {'model': Value('string'), 'backend': Value('string'), 'dimension': Value('int64'), 'dtype': Value('string'), 'normalized': Value('bool'), 'denseOnly': Value('bool'), 'queryPrefix': Value('string'), 'corpusPrefix': Value('string'), 'maxLength': Value('int64')}, 'files': List({'role': Value('string'), 'path': Value('string'), 'sizeBytes': Value('int64'), 'sha256': Value('string')}), 'android': {'embeddingMode': Value('string'), 'requiresOnnxForQueryEmbedding': Value('bool'), 'recommendedInstallType': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              model: string
              backend: string
              input: string
              outDir: string
              vectors: string
              metadata: string
              count: int64
              allocatedCount: int64
              dimension: int64
              dtype: string
              normalized: bool
              denseOnly: bool
              corpusPrefix: string
              queryPrefix: string
              maxLength: int64
              batchSize: int64
              fp16Inference: bool
              device: string
              startedAt: timestamp[s]
              completedAt: timestamp[s]
              android: struct<embeddingMode: string, requiresOnnxForQueryEmbedding: bool, recommendedInstallType: string>
                child 0, embeddingMode: string
                child 1, requiresOnnxForQueryEmbedding: bool
                child 2, recommendedInstallType: string
              description: string
              id: string
              files: list<item: struct<role: string, path: string, sizeBytes: int64, sha256: string>>
                child 0, item: struct<role: string, path: string, sizeBytes: int64, sha256: string>
                    child 0, role: string
                    child 1, path: string
                    child 2, sizeBytes: int64
                    child 3, sha256: string
              name: string
              version: string
              locale: string
              license: string
              embedding: struct<model: string, backend: string, dimension: int64, dtype: string, normalized: bool, denseOnly: (... 67 chars omitted)
                child 0, model: string
                child 1, backend: string
                child 2, dimension: int64
                child 3, dtype: string
                child 4, normalized: bool
                child 5, denseOnly: bool
                child 6, queryPrefix: string
                child 7, corpusPrefix: string
                child 8, maxLength: int64
              schemaVersion: string
              level: string
              recipeCount: int64
              chunkCount: int64
              createdAt: timestamp[s]
              to
              {'schemaVersion': Value('string'), 'id': Value('string'), 'name': Value('string'), 'version': Value('string'), 'description': Value('string'), 'level': Value('string'), 'createdAt': Value('timestamp[s]'), 'locale': Value('string'), 'license': Value('string'), 'recipeCount': Value('int64'), 'chunkCount': Value('int64'), 'embedding': {'model': Value('string'), 'backend': Value('string'), 'dimension': Value('int64'), 'dtype': Value('string'), 'normalized': Value('bool'), 'denseOnly': Value('bool'), 'queryPrefix': Value('string'), 'corpusPrefix': Value('string'), 'maxLength': Value('int64')}, 'files': List({'role': Value('string'), 'path': Value('string'), 'sizeBytes': Value('int64'), 'sha256': Value('string')}), 'android': {'embeddingMode': Value('string'), 'requiresOnnxForQueryEmbedding': Value('bool'), 'recommendedInstallType': Value('string')}}
              because column names don't match

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ChiShenMe BGE-M3 Recipe Dataset Packs

This repository contains offline recipe RAG dataset packs for the Android-first app 吃什么.

The packs are generated from recipe_corpus_full.json, preprocessed into recipe chunks, and embedded with BAAI/bge-m3 dense vectors.

Files 

dataset-index.json

chishenme-bge-m3-lite-10k/
  dataset-pack.json
  files/
    vectors.f32
    metadata.jsonl
    embedding-manifest.json

chishenme-bge-m3-medium-100k/
  dataset-pack.json
  files/
    vectors.f32
    metadata.jsonl
    embedding-manifest.json

chishenme-bge-m3-full/
  dataset-pack.json
  files/
    vectors.f32
    metadata.jsonl
    embedding-manifest.json

models/
  bge-m3-query-onnx/
    model-pack.json
    files/
      tokenizer.onnx
      model.onnx
      model.onnx_data
      config.json
      tokenizer.json
      sentencepiece.bpe.model

Variants

Level Chunks Use case
Lite 10,000 Fast Android download and smoke tests
Medium 100,000 Beta testing and better recipe coverage
Full 1,552,596 Full offline recipe retrieval

Android Usage

The app downloads a dataset-pack.json file, then downloads the referenced files under files/.

Example manifest URL:

https://huggingface.co/datasets/{username}/chishenme-datasets/resolve/main/chishenme-bge-m3-lite-10k/dataset-pack.json

Replace {username} with the actual Hugging Face namespace.

The Android ONNX query model pack is available at:

https://huggingface.co/datasets/{username}/chishenme-datasets/resolve/main/models/bge-m3-query-onnx/model-pack.json

Format

  • vectors.f32: contiguous float32 dense vectors, row-major, normalized.
  • metadata.jsonl: one JSON object per vector row.
  • embedding-manifest.json: embedding generation metadata.
  • dataset-pack.json: app-facing pack manifest with file sizes and SHA256 hashes.
  • model-pack.json: app-facing ONNX query model manifest.

License

The source dataset is user-provided. Verify redistribution rights before publishing publicly.
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