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tessl
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pypipkg:pypi/deeplake@4.3.x
tile.json

tessl/pypi-deeplake

tessl install tessl/pypi-deeplake@4.3.0

Database for AI powered by a storage format optimized for deep-learning applications.

Agent Success

Agent success rate when using this tile

75%

Improvement

Agent success rate improvement when using this tile compared to baseline

1.6x

Baseline

Agent success rate without this tile

47%

rubric.jsonevals/scenario-2/

{
  "context": "This evaluation assesses the engineer's proficiency in using Deep Lake's data import and export capabilities. The focus is on correct usage of Deep Lake's from_csv(), from_parquet(), and to_csv() functions to handle data ingestion and export operations.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "CSV Import Usage",
      "description": "Uses deeplake.from_csv() to import CSV data. The function should be called with the CSV file path and output dataset path parameters.",
      "max_score": 30
    },
    {
      "name": "Parquet Import Usage",
      "description": "Uses deeplake.from_parquet() to import Parquet data. The function should be called with the Parquet file path and output dataset path parameters.",
      "max_score": 30
    },
    {
      "name": "CSV Export Usage",
      "description": "Uses dataset.to_csv() method to export dataset to CSV format. Should open the dataset first using deeplake.open() or deeplake.open_read_only(), then call to_csv() with the output path.",
      "max_score": 25
    },
    {
      "name": "Dataset Opening",
      "description": "Uses deeplake.open() or deeplake.open_read_only() to open existing datasets before performing operations on them.",
      "max_score": 15
    }
  ]
}