add daatset script and readme

README.md

@@ -1,3 +1,62 @@
 ---
-license: mit
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: OpenAI HumanEval-Infilling
+source_datasets:
+- original
+task_categories:
+- text2text-generation
+task_ids:
+- text2text-generation-other-code-generation
 ---
+
+# HumanEval-Infilling
+
+
+## Dataset Description
+
+- **Homepage:**  https://github.com/openai/human-eval-infilling
+- **Repository:**  https://github.com/openai/human-eval-infilling
+- **Paper:** https://arxiv.org/pdf/2207.14255
+
+## Dataset Summary
+
+[HumanEval-Infilling](https://github.com/openai/human-eval-infilling) is a benchmark for infilling tasks, derived from [HumanEval]() benchmark for the evaluation of code generation models.
+
+## Dataset Structure
+To load the dataset you need to specify a subset among the 5 exiting languages  `[python, cpp, go, java, js]`. By default `python` is loaded. 
+
+```python
+from datasets import load_dataset
+ds = load_dataset("humaneval_infilling", "HumanEval-RandomSpanInfilling")
+
+DatasetDict({
+    test: Dataset({
+        features: ['task_id', 'entry_point', 'prompt', 'suffix', 'canonical_solution', 'test'],
+        num_rows: 1640
+    })
+})
+```
+By default `HumanEval-SingleLineInfilling` subset is loaded.
+## Subsets
+
+This dataset has 4 subsets: HumanEval-MultiLineInfilling, HumanEval-SingleLineInfilling, HumanEval-RandomSpanInfilling, HumanEval-RandomSpanInfillingLight.
+The single-line, multi-line, random span infilling and its light version have 1033, 5815, 1640 and 164 tasks, respectively.
+
+## Citation
+
+```@article{bavarian2022efficient,
+  title={Efficient Training of Language Models to Fill in the Middle},
+  author={Bavarian, Mohammad and Jun, Heewoo and Tezak, Nikolas and Schulman, John and McLeavey, Christine and Tworek, Jerry and Chen, Mark},
+  journal={arXiv preprint arXiv:2207.14255},
+  year={2022}
+}
+```

humaneval_infilling.py

@@ -0,0 +1,80 @@
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@article{bavarian2022efficient,
+  title={Efficient Training of Language Models to Fill in the Middle},
+  author={Bavarian, Mohammad and Jun, Heewoo and Tezak, Nikolas and Schulman, John and McLeavey, Christine and Tworek, Jerry and Chen, Mark},
+  journal={arXiv preprint arXiv:2207.14255},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = """\
+An evaluation benchamrk for infilling tasks on HumanEval dataset for code generation.
+"""
+
+_SUBSETS = [ "MultiLineInfilling", "SingleLineInfilling", "RandomSpanInfilling", "RandomSpanInfillingLight" ]
+
+
+class HumanevalConfig(datasets.BuilderConfig):
+    """BuilderConfig for HumanevalConfig."""
+
+    def __init__(
+        self,
+        subset,
+        **kwargs,
+    ):
+        self.subset = subset
+        name = f"HumanEval-{subset}"
+        kwargs["name"] = name
+        super(HumanevalConfig, self).__init__(**kwargs)
+
+
+class MultiPLE(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = HumanevalConfig
+
+    BUILDER_CONFIGS = [
+        HumanevalConfig(
+            subset=subset,
+            version=datasets.Version("1.0.0"))
+        for subset in _SUBSETS 
+    ]
+
+    DEFAULT_CONFIG_NAME = "HumanEval-SingleLineInfilling"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            license="MIT",
+            features = datasets.Features({'task_id': datasets.Value(dtype='string'),
+                                        'entry_point': datasets.Value(dtype='string'),
+                                        'prompt': datasets.Value(dtype='string'),
+                                        'suffix': datasets.Value(dtype='string'),
+                                        'canonical_solution': datasets.Value(dtype='string'),
+                                        'test': datasets.Value(dtype='string')}),
+            supervised_keys=None,
+            homepage="https://github.com/openai/human-eval-infilling",
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        files = dl_manager.download(
+            f"data/{self.config.name}.jsonl"
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": files,
+                }
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath) as f:
+            for id, line in enumerate(f):
+                row = json.loads(line)
+                yield id, row