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Datasets:
bunyaminergen
/
Cornstack-Python-V1-Filtered

Tasks:
Question Answering
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
100K - 1M
Tags:
code
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
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1
Dataset Viewer (First 5GB)
Auto-converted to Parquet Duplicate
query
stringlengths
33
521
document
stringlengths
8
49.6k
metadata
dict
negatives
sequencelengths
5
101
negative_scores
sequencelengths
5
101
document_score
stringlengths
3
10
document_rank
stringclasses
102 values
Compute the matrixvector product y = Cu where C is a circulant matrix All matrices are real
def circulant_multiplication(u, a): return real(ifft(fft(a)*fft(u)))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def covar(fx,cx):\n \n fx = np.array(fx)\n cx = np.array(cx)\n \n shape_fx = fx.shape\n shape_cx = cx.shape\n \n \n if shape_fx[1] != shape_cx[0]:\n print('-----------------------------------------')\n print(\"Shapes of fx and cx cannot be multiplied:\")\n print(shap...
[ "0.650418", "0.650212", "0.6441079", "0.6313763", "0.6310517", "0.62949276", "0.62782884", "0.62631303", "0.61975265", "0.6096459", "0.608041", "0.606508", "0.6038961", "0.6011421", "0.60068315", "0.59920776", "0.59303707", "0.58836865", "0.5879482", "0.58772385", "0.58575416...
0.6389226
3
Compute the matrixvector product y = Tu where T is a Toeplitz matrix All matrices are real
def toeplitz_multiplication(u, c, r=None): n = len(u) if r is None: r = c u1 = zeros((2*n)) u1[0:n] = u c = np.concatenate((c, [0], r[-1:0:-1])) y1 = circulant_multiplication(u1, c) return y1[0:n]
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def matrix_vector_prod(m,u):\n each_product = []\n for v in m:\n each_product.append(dot_prod(v, u))\n return each_product", "def matmul(x, y):\n if len(list(y.size())) == 2:\n # if one of them is a vector (i.e. wanting to do MV mult)\n z = torch.zeros(2, x.size()[1], dtype=torch...
[ "0.7003199", "0.6513981", "0.64759356", "0.6454179", "0.6377554", "0.6326698", "0.6245358", "0.620894", "0.6208685", "0.61977005", "0.6195611", "0.61694974", "0.6168602", "0.6134469", "0.6106113", "0.60868716", "0.6082444", "0.60823506", "0.6070701", "0.60688484", "0.6063607"...
0.63380134
5
Read in labels from digitStruct.mat file to create a dict of image file name and corresponding labels
def read_labels(digitstruct_file): labels = dict() for dsObj in tdqm(yieldNextDigitStruct(digitstruct_file), ncols=50): image_labels = [] for bbox in dsObj.bboxList: image_labels.append(bbox.label) labels[dsObj.name] = image_labels return labels
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _get_imagenet_as_dict(self):\n real_file_path = os.path.realpath(self.map_file)\n if not os.path.exists(real_file_path):\n raise IOError(\"map file {} not exists\".format(self.map_file))\n\n label_dict = {}\n with open(real_file_path) as fp:\n line = fp.readlin...
[ "0.7442581", "0.67145514", "0.6680717", "0.66700083", "0.6651974", "0.6599294", "0.65706545", "0.6568262", "0.65624034", "0.65466106", "0.6527709", "0.65229243", "0.65100825", "0.6500305", "0.649048", "0.6466592", "0.6466018", "0.6442053", "0.6429563", "0.6409631", "0.6398935...
0.8415674
0
"ref CLRS pg326, solution to the basic supply chain problem using the book notation for variables na(...TRUNCATED)
"def fastestWay( a, t, e, x, n ):\n import pdb;pdb.set_trace() \n f1.append( ( e[0] , 1 ) )\n (...TRUNCATED)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
["def exercise_b2_39():\r\n pass","def exercise_b2_113():\r\n pass","def exercise_b2_93():\r\n(...TRUNCATED)
["0.5789567","0.5612758","0.56002617","0.5582453","0.5527549","0.5454671","0.5450963","0.5440792","0(...TRUNCATED)
0.0
-1
This function computes the fundamental matrix by computing the SVD of Ax = 0 ; 8point algorithm
"def computeFundamentalMatrix(pts1, pts2):\n A = np.empty((8, 9))\n for i in range(len(pts1)-1(...TRUNCATED)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
["def svd0(A):\n M,N = A.shape\n if M>N: return sla.svd(A, full_matrices=True)\n else: return s(...TRUNCATED)
["0.66048837","0.6466162","0.6259937","0.6250825","0.62505597","0.62274474","0.6104567","0.6089218",(...TRUNCATED)
0.68444854
0
"Leverages the 8point algorithm and implement RANSAC algorithm to find the inliers and the best fund(...TRUNCATED)
"def getInlierRANSAC(pts1, pts2):\n # global finalFundamentalMatrix\n iterations = 50\n thr(...TRUNCATED)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
["def ransac(data, hypothesis, metric, sample_size, num_iter, inlier_thresh):\n N,d = data.shape\(...TRUNCATED)
["0.6203597","0.5916464","0.5894118","0.5867515","0.5715989","0.56956524","0.56905115","0.5686345","(...TRUNCATED)
0.699575
0
"=========================================================== DateFormatedSQL(x) ====================(...TRUNCATED)
"def DateFormatedSQL(x):\n x=[i[0] for i in x]\n \n x1=[]\n for i in x:\n if len((...TRUNCATED)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
["def change_format_from_input_to_datetime(list_d_t_t):\n data_output = []\n\n for row in list(...TRUNCATED)
["0.66734904","0.65000284","0.6259414","0.59757656","0.5600508","0.5579302","0.5578522","0.5551475",(...TRUNCATED)
0.7940835
0
"=========================================================== dateformated(x) =======================(...TRUNCATED)
"def DateFormated(x):\n \n x1=[]\n for i in x:\n if len(i)==19:\n x1.appe(...TRUNCATED)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
["def change_format_from_input_to_datetime(list_d_t_t):\n data_output = []\n\n for row in list(...TRUNCATED)
["0.73220545","0.6644235","0.64673054","0.63785565","0.6323779","0.63159305","0.6256937","0.6174311"(...TRUNCATED)
0.75249213
0
Mimic the & operator in R. This has to have Expression objects to be involved to work
"def _op_and_(self, left: Any, right: Any) -> Any:\n if isinstance(left, list):\n (...TRUNCATED)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
["def AND(f, g):\n def _and(x):\n return f(x) & g(x)\n return _and","def and_(a, b):","(...TRUNCATED)
["0.6913351","0.6844835","0.6834847","0.68041515","0.6614185","0.6585983","0.65602845","0.65299505",(...TRUNCATED)
0.62697506
20
Mimic the & operator in R. This has to have Expression objects to be involved to work
"def _op_or_(self, left: Any, right: Any) -> Any:\n if isinstance(left, list):\n r(...TRUNCATED)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
["def AND(f, g):\n def _and(x):\n return f(x) & g(x)\n return _and","def and_(a, b):","(...TRUNCATED)
["0.6913351","0.6844835","0.6834847","0.68041515","0.6614185","0.6585983","0.65602845","0.65299505",(...TRUNCATED)
0.0
-1
End of preview. Expand in Data Studio
YAML Metadata Warning: The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Cornstack Python v1 Filtered

The Cornstack Python v1 Filtered dataset is derived from the nomic-ai/cornstack-python-v1 dataset by limiting queries to a maximum of 17 words and restricting the total number of rows to 423259. This dataset is suitable for Python programming education and question-answering applications.

Note: If you would like to contribute to this repository, please read the CONTRIBUTING first.

TableofContents

Features

  • Name: Cornstack Python v1 Filtered
  • Primary Purpose: Contains query-document pairs with corresponding Python code implementations, focusing primarily on matrix and vector operations (e.g., matrix-vector multiplication, circulant and Toeplitz matrices), along with associated negative samples for machine learning tasks like code retrieval and similarity modeling.
  • Language: English
  • Format: JSONL
  • License: GPL-3.0

File Structure 

.
├── CONTRIBUTING.md
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── shard-00.jsonl.gz
├── shard-01.jsonl.gz
├── shard-02.jsonl.gz
├── shard-03.jsonl.gz
└── shard-04.jsonl.gz

1 directory, 10 files

Metadata

Data Dictionary

The dataset contains pairs of queries and documents with associated metadata, negative examples, and scoring information.

CSV
Column Description Type
query Textual query or instruction string
document Relevant code snippet or textual response string
negatives List of non-relevant code snippets list[string]
metadata JSON object containing additional structured information JSON object
negative_scores List of scores corresponding to each negative example list[float]
document_score Score for the document float
document_rank Rank or category label for the document string
Example row (CSV):
query document negatives negative_scores document_score document_rank metadata
Compute the matrixvector product y = Cu where C is a circulant matrix All matrices are real def circulant_multiplication(u, a): return real(ifft(fft(a)*fft(u))) ['def covar(fx,cx): ...', 'def matmul(self, q: np.ndarray): ...'] [0.7675772, 0.6984068] 0.69579995 2 {"objective": {"self": [], "paired": [], "triplet": [["query", "document", "negatives"]]}}
JSON Lines

Each line represents one JSON object with the following structure:

{
  "query": "string, textual query or instruction",
  "document": "string, relevant code snippet or textual response",
  "negatives": ["list of strings, non-relevant code snippets"],
  "negative_scores": ["list of floats, scores for each negative example"],
  "document_score": "float, score for the document",
  "document_rank": "string, rank or category label",
  "metadata": {
      "objective": {
          "self": "list, self-related metadata (often empty)",
          "paired": "list, pairwise metadata (often empty)",
          "triplet": [["query", "document", "negatives"]]
      }
  }
}
Example row (JSONL): 
{
  "query":"Compute the matrixvector product y = Cu where C is a circulant matrix All matrices are real",
  "document":"def circulant_multiplication(u, a): return real(ifft(fft(a)*fft(u)))",
  "negatives":[
    "def covar(fx,cx): ...",
    "def __matmul__(self, q: np.ndarray): ..."
  ],
  "negative_scores":[
    0.7675772,
    0.6984068
  ],
  "document_score":0.69579995,
  "document_rank":"2",
  "metadata":{
    "objective":{
      "self":[ ],
      "paired":[ ],
      "triplet":[
        [
          "query",
          "document",
          "negatives"
        ]
      ]
    }
  }
}

Usage

Hugging Face 
from datasets import load_dataset

# 141k:
dataset_141k = load_dataset("bunyaminergen/cornstack-python-v1-filtered", revision="v3", split="train")
print(dataset_141k[0])

# 282k:
dataset_282k = load_dataset("bunyaminergen/cornstack-python-v1-filtered", revision="v5", split="train")
print(dataset_282k[0])

# 423k:
dataset_423k = load_dataset("bunyaminergen/cornstack-python-v1-filtered", revision="v7", split="train")
print(dataset_423k[0])

Versioning

  • v3: 141k version
  • v5: 282k version
  • v7: 423k version

Licence

Team

Contact

Reference

This dataset is derived from the original dataset nomic-ai/cornstack-python-v1.

Citation 

@misc{           CornstackPythonv1Filtered,
  author       = {Bunyamin Ergen},
  title        = {CornstackPythonv1Filtered},
  year         = {2025},
  month        = {03},
  url          = {https://huggingface.co/datasets/bunyaminergen/cornstack-python-v1-filtered},
}
Downloads last month
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Size of the auto-converted Parquet files (First 5GB):
1.98 GB
Number of rows (First 5GB):
82,011
Estimated number of rows:
420,637