--- dataset_info: - config_name: issue_comments features: - name: user dtype: string - name: created_at dtype: timestamp[us] - name: body dtype: string - name: issue_number dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4293281 num_examples: 8587 download_size: 1725256 dataset_size: 4293281 - config_name: issues features: - name: number dtype: int64 - name: title dtype: string - name: user dtype: string - name: state dtype: string - name: created_at dtype: timestamp[us] - name: closed_at dtype: timestamp[us] - name: comments_count dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 292185 num_examples: 2550 download_size: 173645 dataset_size: 292185 - config_name: models features: - name: id dtype: string - name: created_at dtype: timestamp[us, tz=UTC] - name: likes dtype: int64 - name: downloads dtype: int64 - name: tags sequence: string splits: - name: train num_bytes: 15214429 num_examples: 47486 download_size: 2248615 dataset_size: 15214429 - config_name: models_likes features: - name: user dtype: string - name: model_id dtype: string - name: liked_at dtype: timestamp[s, tz=UTC] splits: - name: train num_bytes: 342388.0 num_examples: 5572 download_size: 155361 dataset_size: 342388.0 - config_name: pypi_downloads features: - name: day dtype: date32 - name: num_downloads dtype: int64 splits: - name: train num_bytes: 19440.0 num_examples: 1620 download_size: 15062 dataset_size: 19440.0 - config_name: stargazers features: - name: starred_at dtype: timestamp[s, tz=UTC] - name: user dtype: string splits: - name: train num_bytes: 231942 num_examples: 10849 download_size: 224968 dataset_size: 231942 configs: - config_name: issue_comments data_files: - split: train path: issue_comments/train-* - config_name: issues data_files: - split: train path: issues/train-* - config_name: models data_files: - split: train path: models/train-* - config_name: models_likes data_files: - split: train path: models_likes/train-* - config_name: pypi_downloads data_files: - split: train path: pypi_downloads/train-* - config_name: stargazers data_files: - split: train path: stargazers/train-* --- ## Stars ```python import requests from datetime import datetime from datasets import Dataset import pyarrow as pa import os def get_stargazers(owner, repo, token): # Initialize the count and the page number page = 1 stargazers = [] while True: # Construct the URL for the stargazers with pagination stargazers_url = f"https://api.github.com/repos/{owner}/{repo}/stargazers?page={page}&per_page=100" # Send the request to GitHub API with appropriate headers headers = {"Accept": "application/vnd.github.v3.star+json", "Authorization": "token " + token} response = requests.get(stargazers_url, headers=headers) if response.status_code != 200: raise Exception(f"Failed to fetch stargazers with status code {response.status_code}: {response.text}") stargazers_page = response.json() if not stargazers_page: # Exit the loop if there are no more stargazers to process break stargazers.extend(stargazers_page) page += 1 # Move to the next page return stargazers token = os.environ.get("GITHUB_PAT") stargazers = get_stargazers("huggingface", "trl", token) stargazers = {key: [stargazer[key] for stargazer in stargazers] for key in stargazers[0].keys()} dataset = Dataset.from_dict(stargazers) def clean(example): starred_at = datetime.strptime(example["starred_at"], "%Y-%m-%dT%H:%M:%SZ") starred_at = pa.scalar(starred_at, type=pa.timestamp("s", tz="UTC")) return {"starred_at": starred_at, "user": example["user"]["login"]} dataset = dataset.map(clean, remove_columns=dataset.column_names) dataset.push_to_hub("qgallouedec/trl-metrics", config_name="stargazers") ``` ## Pypi downloads ```python from datasets import Dataset from google.cloud import bigquery import os os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "propane-tree-432413-4c3e2b5e6b3c.json" # Initialize a BigQuery client client = bigquery.Client() # Define your query query = """ #standardSQL WITH daily_downloads AS ( SELECT DATE(timestamp) AS day, COUNT(*) AS num_downloads FROM `bigquery-public-data.pypi.file_downloads` WHERE file.project = 'trl' -- Filter for the last 12 months AND DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 54 MONTH) AND CURRENT_DATE() GROUP BY day ) SELECT day, num_downloads FROM daily_downloads ORDER BY day DESC """ # Execute the query query_job = client.query(query) # Fetch the results results = query_job.result() # Convert the results to a pandas DataFrame and then to a Dataset df = results.to_dataframe() dataset = Dataset.from_pandas(df) dataset.push_to_hub("qgallouedec/trl-metrics", config_name="pypi_downloads") ``` ## Models tagged ```python from huggingface_hub import HfApi from datasets import Dataset api = HfApi() models = api.list_models(tags="trl") dataset_list = [{"id": model.id, "created_at": model.created_at, "likes": model.likes, "downloads": model.downloads, "tags": model.tags} for model in models] dataset_dict = {key: [d[key] for d in dataset_list] for key in dataset_list[0].keys()} dataset = Dataset.from_dict(dataset_dict) dataset.push_to_hub("qgallouedec/trl-metrics", config_name="models") ``` ## Issues and comments ```python import requests from datetime import datetime import os from datasets import Dataset from tqdm import tqdm token = os.environ.get("GITHUB_PAT") def get_full_response(url, headers, params=None): page = 1 output = [] params = params or {} while True: params = {**params, "page": page, "per_page": 100} response = requests.get(url, headers=headers, params=params) if response.status_code != 200: raise Exception(f"Failed to fetch issues: {response.text}") batch = response.json() if len(batch) == 0: break output.extend(batch) page += 1 return output # GitHub API URL for issues (closed and open) issues_url = f"https://api.github.com/repos/huggingface/trl/issues" # Set up headers for authentication headers = {"Authorization": f"token {token}", "Accept": "application/vnd.github.v3+json"} # Make the request issues = get_full_response(issues_url, headers, params={"state": "all"}) issues_dataset_dict = { "number": [], "title": [], "user": [], "state": [], "created_at": [], "closed_at": [], "comments_count": [], } comments_dataset_dict = { "user": [], "created_at": [], "body": [], "issue_number": [], } for issue in tqdm(issues): # Extract relevant information issue_number = issue["number"] title = issue["title"] created_at = datetime.strptime(issue["created_at"], "%Y-%m-%dT%H:%M:%SZ") comments_count = issue["comments"] comments_url = issue["comments_url"] comments = get_full_response(comments_url, headers=headers) for comment in comments: comments_dataset_dict["user"].append(comment["user"]["login"]) comments_dataset_dict["created_at"].append(datetime.strptime(comment["created_at"], "%Y-%m-%dT%H:%M:%SZ")) comments_dataset_dict["body"].append(comment["body"]) comments_dataset_dict["issue_number"].append(issue_number) issues_dataset_dict["number"].append(issue_number) issues_dataset_dict["title"].append(title) issues_dataset_dict["user"].append(issue["user"]["login"]) issues_dataset_dict["state"].append(issue["state"]) issues_dataset_dict["created_at"].append(datetime.strptime(issue["created_at"], "%Y-%m-%dT%H:%M:%SZ")) issues_dataset_dict["closed_at"].append(datetime.strptime(issue["closed_at"], "%Y-%m-%dT%H:%M:%SZ") if issue["closed_at"] else None) issues_dataset_dict["comments_count"].append(comments_count) issues_dataset = Dataset.from_dict(issues_dataset_dict) comments_dataset = Dataset.from_dict(comments_dataset_dict) issues_dataset.push_to_hub("qgallouedec/trl-metrics", config_name="issues") comments_dataset.push_to_hub("qgallouedec/trl-metrics", config_name="issue_comments") ```