Duplicate from google-research-datasets/mbpp

Co-authored-by: Parquet-converter (BOT) <[email protected]>

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,276 @@
+---
+annotations_creators:
+- crowdsourced
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text2text-generation
+task_ids: []
+pretty_name: Mostly Basic Python Problems
+tags:
+- code-generation
+dataset_info:
+- config_name: full
+  features:
+  - name: task_id
+    dtype: int32
+  - 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
+  splits:
+  - name: train
+    num_bytes: 176879
+    num_examples: 374
+  - name: test
+    num_bytes: 244104
+    num_examples: 500
+  - name: validation
+    num_bytes: 42405
+    num_examples: 90
+  - name: prompt
+    num_bytes: 4550
+    num_examples: 10
+  download_size: 236069
+  dataset_size: 467938
+- config_name: sanitized
+  features:
+  - name: source_file
+    dtype: string
+  - name: task_id
+    dtype: int32
+  - name: prompt
+    dtype: string
+  - name: code
+    dtype: string
+  - name: test_imports
+    sequence: string
+  - name: test_list
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 63453
+    num_examples: 120
+  - name: test
+    num_bytes: 132720
+    num_examples: 257
+  - name: validation
+    num_bytes: 20050
+    num_examples: 43
+  - name: prompt
+    num_bytes: 3407
+    num_examples: 7
+  download_size: 115422
+  dataset_size: 219630
+configs:
+- config_name: full
+  data_files:
+  - split: train
+    path: full/train-*
+  - split: test
+    path: full/test-*
+  - split: validation
+    path: full/validation-*
+  - split: prompt
+    path: full/prompt-*
+  default: true
+- config_name: sanitized
+  data_files:
+  - split: train
+    path: sanitized/train-*
+  - split: test
+    path: sanitized/test-*
+  - split: validation
+    path: sanitized/validation-*
+  - split: prompt
+    path: sanitized/prompt-*
+---
+
+# Dataset Card for Mostly Basic Python Problems (mbpp)
+
+## Table of Contents
+- [Dataset Card for Mostly Basic Python Problems (mbpp)](#dataset-card-for-mostly-basic-python-problems-(mbpp))
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+- **Repository:** https://github.com/google-research/google-research/tree/master/mbpp
+- **Paper:** [Program Synthesis with Large Language Models](https://arxiv.org/abs/2108.07732)
+
+### Dataset Summary
+The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. 
+
+Released [here](https://github.com/google-research/google-research/tree/master/mbpp) as part of [Program Synthesis with Large Language Models, Austin et. al., 2021](https://arxiv.org/abs/2108.07732).
+
+### Supported Tasks and Leaderboards
+This dataset is used to evaluate code generations.
+
+### Languages
+English - Python code
+
+## Dataset Structure
+
+```python
+dataset_full = load_dataset("mbpp")
+DatasetDict({
+    test: Dataset({
+        features: ['task_id', 'text', 'code', 'test_list', 'test_setup_code', 'challenge_test_list'],
+        num_rows: 974
+    })
+})
+
+dataset_sanitized = load_dataset("mbpp", "sanitized")
+DatasetDict({
+    test: Dataset({
+        features: ['source_file', 'task_id', 'prompt', 'code', 'test_imports', 'test_list'],
+        num_rows: 427
+    })
+})
+```
+
+### Data Instances
+
+#### mbpp - full
+```
+{
+    'task_id': 1,
+    'text': 'Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].',
+    'code': 'R = 3\r\nC = 3\r\ndef min_cost(cost, m, n): \r\n\ttc = [[0 for x in range(C)] for x in range(R)] \r\n\ttc[0][0] = cost[0][0] \r\n\tfor i in range(1, m+1): \r\n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \r\n\tfor j in range(1, n+1): \r\n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \r\n\tfor i in range(1, m+1): \r\n\t\tfor j in range(1, n+1): \r\n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \r\n\treturn tc[m][n]',
+    'test_list': [
+        'assert min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8',
+        'assert min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12',
+        'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'],
+    'test_setup_code': '',
+    'challenge_test_list': []
+}
+```
+#### mbpp - sanitized
+```
+{
+    'source_file': 'Benchmark Questions Verification V2.ipynb',
+    'task_id': 2,
+    'prompt': 'Write a function to find the shared elements from the given two lists.',
+    'code': 'def similar_elements(test_tup1, test_tup2):\n  res = tuple(set(test_tup1) & set(test_tup2))\n  return (res) ',
+    'test_imports': [],
+    'test_list': [
+        'assert set(similar_elements((3, 4, 5, 6),(5, 7, 4, 10))) == set((4, 5))',
+        'assert set(similar_elements((1, 2, 3, 4),(5, 4, 3, 7))) == set((3, 4))',
+        'assert set(similar_elements((11, 12, 14, 13),(17, 15, 14, 13))) == set((13, 14))'
+        ]
+}
+```
+### Data Fields
+
+- `source_file`: unknown
+- `text`/`prompt`: description of programming task
+- `code`: solution for programming task
+- `test_setup_code`/`test_imports`: necessary code imports to execute tests
+- `test_list`: list of tests to verify solution
+- `challenge_test_list`: list of more challenging test to further probe solution
+
+### Data Splits
+There are two version of the dataset (full and sanitized), each with four splits:
+- train
+- evaluation
+- test
+- prompt
+
+The `prompt` split corresponds to samples used for few-shot prompting and not for training.
+
+## Dataset Creation
+See section 2.1 of original [paper](https://arxiv.org/abs/2108.07732).
+
+### Curation Rationale
+In order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+The dataset was manually created from scratch.
+
+#### Who are the source language producers?
+The dataset was created with an internal crowdsourcing effort at Google.
+
+### Annotations
+
+#### Annotation process
+The full dataset was created first and a subset then underwent a second round to improve the task descriptions.
+
+#### Who are the annotators?
+The dataset was created with an internal crowdsourcing effort at Google.
+
+### Personal and Sensitive Information
+None.
+
+## Considerations for Using the Data
+Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful.
+
+### Social Impact of Dataset
+With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
+
+### Discussion of Biases
+
+### Other Known Limitations
+Since the task descriptions might not be expressive enough to solve the task. The `sanitized` split aims at addressing this issue by having a second round of annotators improve the dataset.
+
+## Additional Information
+
+### Dataset Curators
+Google Research
+
+### Licensing Information
+CC-BY-4.0
+
+### Citation Information
+```
+@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}
+```
+### Contributions
+Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.

full/prompt-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a053e4bb85ceb77430ae80592addb4ca4dc6ba087592f9e04537800ee88b7431
+size 7878

full/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:566fd53060ffba5766dace1d1e2f4c38906781526de222b0dfbdbc325b696c77
+size 115824

full/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09d125ca31edacb7800be8c67c45abff618faf0214ff551291817d06bdb914ae
+size 87223

full/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f0ec060987432d99fe8fb409d31e6c67445b208a01741c5583517c80a10fe80
+size 25144

sanitized/prompt-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73c623309b7b5d65fd5661204b35f779f8e66301aa9832d1ad4b8fc3b21151fd
+size 6717

sanitized/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9e9efa2c0d59ef5e55537a9d126b8f875d5ac010a8d75628d76824884e15850
+size 60864

sanitized/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d95f8ad6d2fff08fe4826122d6e3e31f75716825d0c5c340d297aca5e9e0de0e
+size 33854

sanitized/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27e065fcab3c863959933328a7fdbf404e1bcb5464b1be6fe0dcd9530e420204
+size 13987