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A_python_files_elaborated_metadata.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46359841e7cf01ebd2232ff24f310644481798ba85138db310dee8fb29417bbd
+size 22895119

C_mega_licensed_corpus_redacted.txt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25872f695fefeb4106ddd7006c798767be925a67abadf5d22da240541cad6717
+size 767255913

README.md

@@ -1,41 +1,20 @@
 ---
 annotations_creators:
-- author
+  - author
 license:
-- gpl-3.0
+  - gpl-3.0
 multilinguality:
-- monolingual
+  - monolingual
 pretty_name: GitHub-Python
 dataset_name: github-python
 dataset_type: code
 tags:
-- code
-- python
+  - code
+  - python
 size_categories:
-- 100K<n⩽1M
+  - 100K<n⩽1M
 task_categories:
-- text-generation
-configs:
-- config_name: default
-  data_files:
-  - split: github_meta_elaborated
-    path: data/github_meta_elaborated-*
-dataset_info:
-  features:
-  - name: repo_owner
-    dtype: string
-  - name: repo_name
-    dtype: string
-  - name: file_path
-    dtype: string
-  - name: file_url
-    dtype: string
-  splits:
-  - name: github_meta_elaborated
-    num_bytes: 24941794
-    num_examples: 186066
-  download_size: 8323253
-  dataset_size: 24941794
+  - text-generation
 ---
 
 # GitHub-Python — Licensed & Elaborated Variants

dataset.py

@@ -1,41 +1,29 @@
-import csv
-import datasets
+from datasets import Dataset
+import pandas as pd
 
-class ElaboratedMetadataConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+a_path = "A_python_files_elaborated_metadata.csv"
+b_path = "B_github_python_metadata.csv"
+c_path = "C_mega_licensed_corpus_redacted.txt"
 
-class ElaboratedMetadata(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        ElaboratedMetadataConfig(name="default", version=datasets.Version("1.0.0"))
-    ]
+df_a = pd.read_csv(a_path)
+df_a = df_a.rename(columns={
+    "owner": "repo_owner",
+    "url": "file_url"
+})
+ds_a = Dataset.from_pandas(df_a)
 
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features({
-                "owner": datasets.Value("string"),
-                "repo_name": datasets.Value("string"),
-                "file_path": datasets.Value("string"),
-                "url": datasets.Value("string"),
-            }),
-        )
+ds_a.push_to_hub("jblitzar/github-python-meta-elaborated", split="train")
 
-    def _split_generators(self, dl_manager):
-        data_path = dl_manager.download("A_python_files_elaborated_metadata.csv")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": data_path},
-            )
-        ]
 
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            for idx, row in enumerate(reader):
-                yield idx, {
-                    "owner": row["owner"],
-                    "repo_name": row["repo_name"],
-                    "file_path": row["file_path"],
-                    "url": row["url"],
-                }
+df_b = pd.read_csv("B_github_python_metadata.csv")
+df_b["repo_url"] = df_b.get("repo_url", "unknown")
+print("Columns in df_b:", df_b.columns)
+from datasets import Dataset
+ds_b = Dataset.from_pandas(df_b)
+ds_b.push_to_hub("jblitzar/github-python-metadata", split="train")
+
+
+with open(c_path, encoding="utf-8", errors="ignore") as f:
+    lines = f.readlines()
+ds_c = Dataset.from_dict({"text": lines})
+ds_c.push_to_hub("jblitzar/github-python-corpus", split="train")