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Dataset to Model Monitor - AI Evaluation & Observability Tool

Overview

Dataset to Model Monitor is a lightweight Hugging Face Space that watches the Hugging Face model hub for new models trained on a specified dataset (examples include HuggingFaceM4 and VQAv2). When the monitor detects a newly published model whose metadata or model card references the target dataset, it leverages the librarian-bot to post an alert into a designated discussion thread on the Space. That lets developers and researchers automatically receive activity notifications without manually scanning model listings. The Space is intended for project maintainers, dataset curators, and researchers who want to track adoption and derivative work for a dataset. Because it runs in a Hugging Face Space and uses the librarian-bot for notifications, no separate server-side infrastructure is required to receive discussion alerts — the workflow runs on the Space and surfaces model additions directly into the Space's discussion area. For the project page and discussion thread, see the Space’s Hugging Face listing and discussion (source: Hugging Face Space discussion thread).

Model Statistics

Likes: 25

Model Details

Technical overview: Dataset to Model Monitor is implemented as a Hugging Face Space that continuously (or periodically) queries the Hugging Face model hub for models associated with a specified dataset name or dataset-related metadata. When a newly observed model references the monitored dataset, the Space uses the librarian-bot integration to create a discussion post/alert in the Space’s discussion thread, enabling downstream subscribers to see the notification in-context. Exact implementation details (polling frequency, matching heuristics for dataset mentions, runtime stack, and authentication scopes used by the librarian-bot) are not published on the Space page. Hub-facing statistics published on the Space show 25 likes and 0 downloads as of the Space listing (source: Hugging Face Space page). Pipeline and parameter counts for this Space/tool are not applicable or are not provided.

Key Features

Automatic detection of new models referencing a specified dataset (e.g., HuggingFaceM4, VQAv2)
Posts alerts into a Hugging Face Space discussion using the librarian-bot integration
Configurable target dataset to monitor via the Space’s settings or inputs
Runs as a Hugging Face Space—no additional server infrastructure required
Designed to help researchers and maintainers track dataset-specific model publications

Example Usage

Example (python):

import requests
import time
import json

# Simple polling example: search the Hugging Face models endpoint for models mentioning a dataset
# Note: This example is illustrative. Adapt polling interval and persistence to your needs.
DATASET_NAME = "VQAv2"
API_URL = f"https://huggingface.co/api/models?search={DATASET_NAME}"
STATE_FILE = "seen_models.json"
POLL_INTERVAL_SECONDS = 300

try:
    with open(STATE_FILE, "r") as f:
        seen = set(json.load(f))
except Exception:
    seen = set()

while True:
    resp = requests.get(API_URL, timeout=20)
    resp.raise_for_status()
    models = resp.json()

    current_ids = {m.get("id") for m in models if m.get("id")}
    new_models = current_ids - seen

    for model_id in sorted(new_models):
        print("New model detected:", model_id)
        print("Model URL:", f"https://huggingface.co/{model_id}")
        # Integration point: post this info to a discussion thread via the librarian-bot or other webhook

    if new_models:
        seen.update(new_models)
        with open(STATE_FILE, "w") as f:
            json.dump(sorted(list(seen)), f)

    time.sleep(POLL_INTERVAL_SECONDS)