Add files using upload-large-folder tool

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- code
+- python
+- long-context
+- coding
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: 0k
+  data_files:
+  - split: test
+    path: 0k/test-*
+  - split: train
+    path: 0k/train-*
+  - split: validation
+    path: 0k/validation-*
+  - split: prompt
+    path: 0k/prompt-*
+- config_name: 1k
+  data_files:
+  - split: test
+    path: 1k/test-*
+  - split: train
+    path: 1k/train-*
+  - split: validation
+    path: 1k/validation-*
+  - split: prompt
+    path: 1k/prompt-*
+- config_name: 2k
+  data_files:
+  - split: test
+    path: 2k/test-*
+  - split: train
+    path: 2k/train-*
+  - split: validation
+    path: 2k/validation-*
+  - split: prompt
+    path: 2k/prompt-*
+- config_name: 4k
+  data_files:
+  - split: test
+    path: 4k/test-*
+  - split: train
+    path: 4k/train-*
+  - split: validation
+    path: 4k/validation-*
+  - split: prompt
+    path: 4k/prompt-*
+- config_name: 16k
+  data_files:
+  - split: test
+    path: 16k/test-*
+  - split: train
+    path: 16k/train-*
+  - split: validation
+    path: 16k/validation-*
+  - split: prompt
+    path: 16k/prompt-*
+- config_name: 32k
+  data_files:
+  - split: test
+    path: 32k/test-*
+  - split: train
+    path: 32k/train-*
+  - split: validation
+    path: 32k/validation-*
+  - split: prompt
+    path: 32k/prompt-*
+- config_name: 64k
+  data_files:
+  - split: test
+    path: 64k/test-*
+  - split: train
+    path: 64k/train-*
+  - split: validation
+    path: 64k/validation-*
+  - split: prompt
+    path: 64k/prompt-*
+- config_name: 128k
+  data_files:
+  - split: test
+    path: 128k/test-*
+  - split: train
+    path: 128k/train-*
+  - split: validation
+    path: 128k/validation-*
+  - split: prompt
+    path: 128k/prompt-*
+- config_name: 256k
+  data_files:
+  - split: test
+    path: 256k/test-*
+  - split: train
+    path: 256k/train-*
+  - split: validation
+    path: 256k/validation-*
+  - split: prompt
+    path: 256k/prompt-*
+- config_name: 512k
+  data_files:
+  - split: test
+    path: 512k/test-*
+  - split: train
+    path: 512k/train-*
+  - split: validation
+    path: 512k/validation-*
+  - split: prompt
+    path: 512k/prompt-*
+- config_name: 1m
+  data_files:
+  - split: test
+    path: 1m/test-*
+  - split: train
+    path: 1m/train-*
+  - split: validation
+    path: 1m/validation-*
+  - split: prompt
+    path: 1m/prompt-*
+dataset_info:
+  features:
+  - name: task_id
+    dtype: int64
+  - name: text
+    dtype: string
+  - name: code
+    dtype: string
+  - name: test_list
+    sequence: string
+  - name: test_setup_code
+    dtype: string
+  - name: challenge_test_list
+    sequence: string
+  - name: context_text
+    dtype: string
+  - name: context_id
+    dtype: string
+  - name: context_length_tokens
+    dtype: int64
+  - name: context_type
+    dtype: string
+  - name: code_length_chars
+    dtype: int64
+  - name: dataset_version
+    dtype: string
+  splits:
+  - name: test
+    num_examples: 500
+  - name: train
+    num_examples: 374
+  - name: validation
+    num_examples: 90
+  - name: prompt
+    num_examples: 10
+---
+
+# MBPP Long-Context Dataset
+
+## Overview
+
+MBPP Long-Context is a benchmark dataset that combines coding problems from the [MBPP (Mostly Basic Python Problems)](https://github.com/google-research/google-research/tree/master/mbpp) dataset with long-context distractors from [BABILong](https://github.com/booydar/babilong). This dataset evaluates code generation performance under long-context conditions, testing whether models can maintain coding ability while processing lengthy contextual information.
+
+## Dataset Description
+
+This dataset contains **974 MBPP problems** (across test/train/validation/prompt splits), each prepended with narrative context from BABILong at various lengths. The dataset preserves the original MBPP split structure to maintain compatibility with existing evaluation protocols.
+
+### Key Features
+
+- **Multiple Context Lengths**: 11 configurations from 0k to 1M tokens
+- **Stratified Assignment**: Contexts are evenly distributed across problem difficulty levels
+- **Split Preservation**: Maintains original MBPP splits (test: 500, train: 374, validation: 90, prompt: 10)
+- **Reproducible**: Fixed seed ensures consistent context assignments
+- **Metadata Rich**: Includes token counts, context IDs, and problem difficulty proxies
+
+## Dataset Configurations
+
+Each configuration represents a different context length:
+
+| Config | Approximate Tokens | BABILong Source |
+|--------|-------------------|-----------------|
+| `0k` | ~85 tokens | 0k baseline |
+| `1k` | ~1,024 tokens | 1k |
+| `2k` | ~2,048 tokens | 2k |
+| `4k` | ~4,096 tokens | 4k |
+| `16k` | ~16,384 tokens | 16k |
+| `32k` | ~32,768 tokens | 32k |
+| `64k` | ~65,536 tokens | 64k |
+| `128k` | ~131,072 tokens | 128k |
+| `256k` | ~262,144 tokens | 256k |
+| `512k` | ~524,288 tokens | 512k |
+| `1m` | ~1,048,576 tokens | 1M |
+
+Token counts are approximate and measured using the Meta-Llama-3-8B tokenizer.
+
+## Dataset Structure
+
+### Data Fields
+
+Each sample contains:
+
+#### Original MBPP Fields
+- `task_id` (int): Unique task identifier
+- `text` (str): Problem description
+- `code` (str): Reference solution
+- `test_list` (List[str]): Test cases (assertions)
+- `test_setup_code` (str): Optional setup code
+- `challenge_test_list` (List[str]): Additional test cases
+
+#### Long-Context Fields
+- `context_text` (str): Prepended distractor text from BABILong
+- `context_id` (str): BABILong source identifier (e.g., "babilong_128k_qa1_sample_42")
+- `context_length_tokens` (int): Token count using Llama tokenizer
+- `context_type` (str): Type of context (currently "narrative")
+
+#### Metadata
+- `code_length_chars` (int): Reference solution length for difficulty tracking
+- `dataset_version` (str): Version tag ("1.0.0")
+
+### Data Splits
+
+All configurations maintain the original MBPP split structure:
+
+- **test**: 500 samples (primary evaluation set)
+- **train**: 374 samples
+- **validation**: 90 samples
+- **prompt**: 10 samples (few-shot examples)
+
+## Dataset Creation
+
+### Context Assignment Methodology
+
+To avoid confounding variables, this dataset uses **stratified random assignment**:
+
+1. **Sort MBPP tasks** by code length (difficulty proxy)
+2. **Extract contexts** from BABILong qa1-qa10 splits (~1000 contexts per length)
+3. **Extend contexts** to match task count (974 samples)
+4. **Shuffle contexts** with fixed seed (unique per split)
+5. **Assign contexts** to sorted tasks
+
+This ensures each context appears evenly across difficulty levels, preventing context-specific bias in evaluation.
+
+## Source Datasets
+
+### MBPP (Mostly Basic Python Problems)
+- **Source**: google-research-datasets/mbpp
+- **License**: Apache 2.0
+- **Size**: 974 problems
+- **Paper**: [Program Synthesis with Large Language Models](https://arxiv.org/abs/2108.07732)
+
+### BABILong
+- **Source**: RMT-team/babilong
+- **License**: MIT
+- **Context Extraction**: `input` field from qa1-qa10 splits
+- **Paper**: [BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://arxiv.org/abs/2406.10149)
+
+
+## Citation
+
+If you use this dataset, please cite both MBPP and BABILong:
+
+```bibtex
+@article{austin2021program,
+  title={Program Synthesis with Large Language Models},
+  author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others},
+  journal={arXiv preprint arXiv:2108.07732},
+  year={2021}
+}
+
+@article{kuratov2024babilong,
+  title={BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack},
+  author={Kuratov, Yuri and Bulatov, Aydar and Anokhin, Petr and Rodkin, Ivan and Sorokin, Dmitriy and Burtsev, Mikhail},
+  journal={arXiv preprint arXiv:2406.10149},
+  year={2024}
+}
+```
+
+## Dataset Card Authors
+
+- Janna Lu ([@jannalu](https://github.com/jannalu))

data/0k/dataset_dict.json

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+{"splits": ["test", "train", "validation", "prompt"]}

data/0k/test/state.json

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data/0k/train/dataset_info.json

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data/0k/train/state.json

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data/0k/validation/dataset_info.json

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data/128k/dataset_dict.json

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data/128k/prompt/dataset_info.json

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data/128k/prompt/state.json

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data/128k/test/dataset_info.json

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data/128k/test/state.json

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data/128k/train/dataset_info.json

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data/128k/train/state.json

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data/128k/validation/dataset_info.json

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data/128k/validation/state.json

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data/1m/dataset_dict.json

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data/1m/prompt/dataset_info.json

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data/1m/test/dataset_info.json

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data/1m/test/state.json

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data/1m/train/dataset_info.json

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+}

data/1m/train/state.json

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data/1m/validation/dataset_info.json

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+}

data/1m/validation/state.json

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data/256k/dataset_dict.json

@@ -0,0 +1 @@
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data/256k/prompt/dataset_info.json

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+}

data/256k/prompt/state.json

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data/256k/test/dataset_info.json

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+}

data/256k/test/state.json

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data/256k/train/dataset_info.json

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+}

data/256k/train/state.json

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data/256k/validation/dataset_info.json

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+}

data/256k/validation/state.json

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data/512k/dataset_dict.json

@@ -0,0 +1 @@
+{"splits": ["test", "train", "validation", "prompt"]}

data/512k/prompt/dataset_info.json

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data/512k/prompt/state.json

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data/512k/test/dataset_info.json

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data/512k/test/state.json

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data/512k/train/dataset_info.json

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data/512k/train/state.json

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data/512k/validation/dataset_info.json

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data/512k/validation/state.json

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data/64k/dataset_dict.json

@@ -0,0 +1 @@
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data/64k/prompt/dataset_info.json

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data/64k/prompt/state.json

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+  "_fingerprint": "59eaccaba9080c49",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

data/64k/test/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "4e2e6ab8457d20a6",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

data/64k/train/dataset_info.json

@@ -0,0 +1,58 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "task_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "code": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "test_list": {
+      "feature": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "_type": "Sequence"
+    },
+    "test_setup_code": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "challenge_test_list": {
+      "feature": {
+        "dtype": "null",
+        "_type": "Value"
+      },
+      "_type": "Sequence"
+    },
+    "context_text": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "context_id": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "context_length_tokens": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "context_type": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "code_length_chars": {
+      "dtype": "int64",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

data/64k/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "7884e63b3025ca63",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

data/64k/validation/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "a9609982eae4707f",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

utils.py

@@ -0,0 +1,126 @@
+import logging
+import re
+from typing import Union
+
+import datasets
+import evaluate as hf_evaluate
+
+
+eval_logger = logging.getLogger(__name__)
+
+
+try:
+    pass_at_k = hf_evaluate.load("code_eval")
+
+    # run simple test to check code execution is enabled before model generation
+    test_cases = ["assert add(2, 3)==5"]
+    candidates = [["def add(a,b): return a*b"]]
+    results = pass_at_k.compute(references=test_cases, predictions=candidates, k=[1])
+except Exception as e:
+    raise e
+
+
+def load_dataset(**kwargs):
+    """Load MBPP long-context dataset with specified context length."""
+    context_length = kwargs.get("context_length", "128k")
+
+    eval_logger.info(
+        f"Loading mbpp_longcontext dataset: context_length={context_length}"
+    )
+    dataset = datasets.load_dataset("jannalu/mbpp-longcontext", name=context_length)
+    return dataset
+
+
+def pass_at_1(
+    references: Union[str, list[str]], predictions: Union[str, list[list[str]]]
+) -> float:
+    """Compute pass@1 metric for code generation."""
+    if isinstance(references, str):
+        references = [references]
+    if isinstance(predictions[0], str):
+        predictions = [[p] for p in predictions]
+    return pass_at_k.compute(
+        references=references,
+        predictions=predictions,
+        k=[1],
+    )[0]["pass@1"]
+
+
+def extract_code_blocks(text: str) -> str:
+    """Extract code from markdown code blocks in generated text."""
+    # Pattern to match ```...``` blocks
+    pattern = r"```(?:\w+)?\n?(.*?)\n?```"
+    # (+ ```) as we add the opening "```python" to the gen_prefix
+    matches = re.findall(pattern, r"```" + text, re.DOTALL)
+    # if no matches, try to match ```...``` blocks (after removing the language)
+    if not matches:
+        text_without_lang = re.sub(r"```python", "```", text)
+        matches = re.findall(pattern, text_without_lang, re.DOTALL)
+    if not matches:
+        return ""
+    else:
+        return matches[0]
+
+
+def build_predictions(resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
+    """Build predictions by extracting code blocks from model responses."""
+    return [[extract_code_blocks(r) for r in resp] for resp in resps]
+
+
+def doc_to_metadata(doc: dict) -> dict:
+    """
+    Extract metadata from a document for tracking and analysis.
+
+    This extracts the context_length_tokens field so results can be
+    grouped and analyzed by sequence length.
+    """
+    return {
+        "seq_length": doc.get("context_length_tokens", 0),
+        "context_id": doc.get("context_id", ""),
+        "context_type": doc.get("context_type", "narrative"),
+    }
+
+
+def list_fewshot_samples():
+    """
+    Return few-shot examples for MBPP long-context.
+    Note: These examples do NOT include the long context since they're used for few-shot.
+    """
+    return [
+        {
+            "task_id": 2,
+            "text": "Write a function to find the similar elements from the given two tuple lists.",
+            "code": "def similar_elements(test_tup1, test_tup2):\r\n  res = tuple(set(test_tup1) & set(test_tup2))\r\n  return (res) ",
+            "test_list": [
+                "assert similar_elements((3, 4, 5, 6),(5, 7, 4, 10)) == (4, 5)",
+                "assert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4)",
+                "assert similar_elements((11, 12, 14, 13),(17, 15, 14, 13)) == (13, 14)",
+            ],
+            "is_fewshot": True,
+            "prompt_with_context": "Here is your task: Write a function to find the similar elements from the given two tuple lists. Your code should pass these tests:\n\nassert similar_elements((3, 4, 5, 6),(5, 7, 4, 10)) == (4, 5)\nassert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4)\nassert similar_elements((11, 12, 14, 13),(17, 15, 14, 13)) == (13, 14)\n[BEGIN]\n",
+        },
+        {
+            "task_id": 3,
+            "text": "Write a python function to identify non-prime numbers.",
+            "code": "import math\r\ndef is_not_prime(n):\r\n    result = False\r\n    for i in range(2,int(math.sqrt(n)) + 1):\r\n        if n % i == 0:\r\n            result = True\r\n    return result",
+            "test_list": [
+                "assert is_not_prime(2) == False",
+                "assert is_not_prime(10) == True",
+                "assert is_not_prime(35) == True",
+            ],
+            "is_fewshot": True,
+            "prompt_with_context": "Here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\nassert is_not_prime(2) == False\nassert is_not_prime(10) == True\nassert is_not_prime(35) == True\n[BEGIN]\n",
+        },
+        {
+            "task_id": 4,
+            "text": "Write a function to find the largest integers from a given list of numbers using heap queue algorithm.",
+            "code": "import heapq as hq\r\ndef heap_queue_largest(nums,n):\r\n  largest_nums = hq.nlargest(n, nums)\r\n  return largest_nums",
+            "test_list": [
+                "assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],3)==[85, 75, 65] ",
+                "assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] ",
+                "assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],5)==[85, 75, 65, 58, 35]",
+            ],
+            "is_fewshot": True,
+            "prompt_with_context": "Here is your task: Write a function to find the largest integers from a given list of numbers using heap queue algorithm. Your code should pass these tests:\n\nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],3)==[85, 75, 65] \nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] \nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],5)==[85, 75, 65, 58, 35]\n[BEGIN]\n",
+        },
+    ]