github_tagger.py

import pandas as pd
dataset_url = "https://git.io/nlp-with-transformers"
df_issues = pd.read_json(dataset_url, lines=True)
print(f"DataFrame shape: {df_issues.shape}")
cols = ["url", "id", "title", "user", "labels", "state", "created_at", "body"]
df_issues.loc[2, cols].to_frame()
df_issues["labels"] = (df_issues["labels"]
.apply(lambda x: [meta["name"] for meta in x]))
df_issues[["labels"]].head()
# print(df_issues)
df_issues["labels"].apply(lambda x : len(x)).value_counts().to_frame().T
df_counts = df_issues["labels"].explode().value_counts()
print(f"Number of labels: {len(df_counts)}")
# Display the top-8 label categories
df_counts.to_frame().head(8).T
label_map = {"Core: Tokenization": "tokenization",
"New model": "new model",
"Core: Modeling": "model training",
"Usage": "usage",
"Core: Pipeline": "pipeline",
"TensorFlow": "tensorflow or tf",
"PyTorch": "pytorch",
"Examples": "examples",
"Documentation": "documentation"}
def filter_labels(x):
    return [label_map[label] for label in x if label in label_map]
df_issues["labels"] = df_issues["labels"].apply(filter_labels)
all_labels = list(label_map.values())