metadata
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
splits:
- name: train
num_bytes: 4153842
num_examples: 8476
download_size: 1656492
dataset_size: 4153842
- 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
splits:
- name: train
num_bytes: 268570
num_examples: 2521
download_size: 157626
dataset_size: 268570
- 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: 13718429
num_examples: 42961
download_size: 2023895
dataset_size: 13718429
- 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: 312436
num_examples: 5077
download_size: 141415
dataset_size: 312436
- config_name: pypi_downloads
features:
- name: day
dtype: date32
- name: num_downloads
dtype: int64
splits:
- name: train
num_bytes: 19428
num_examples: 1619
download_size: 14949
dataset_size: 19428
- config_name: stargazers
features:
- name: starred_at
dtype: timestamp[s, tz=UTC]
- name: user
dtype: string
splits:
- name: train
num_bytes: 224610
num_examples: 10508
download_size: 217947
dataset_size: 224610
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
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
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
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
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")