synthetic-data-generation-of-wikipedia-infoboxes.ipynb

download-hf-dataset-as-jsonl.ipynb

This example demonstrates an ETL pipeline that downloads a HuggingFace dataset of Wikipedia stubs about women in religion and converts it into JSONL files formatted for OpenAI fine-tuning. It shows how to transform raw dataset splits into the chat-based structure with system, user, and assistant roles. The notebook uses the `datasets` library’s `load_dataset` and `to_json` methods to handle training and validation splits separately, saving the output to `/content/`. A custom `create_openai_format` function then wraps each example in the conversational structure required by OpenAI’s API.

72% match

using-pretrained-abstract-to-tweet-model.ipynb

This example shows how a pre-trained transformer model converts academic abstracts into concise, tweet-sized summaries, demonstrating a text-to-text generation workflow. It loads a Hugging Face `AutoModelForSeq2SeqLM` and `AutoTokenizer` from the `andersoncliffb/abstracts_to_tweet_model` repository, then creates a `text2text-generation` pipeline to produce the output. The notebook’s code uses the Transformers library’s pipeline API, with the model and tokenizer specified directly. It does not include a data visualization, instead focusing on the model’s generative output from input text.

60% match

Gist fdb22d9ec25b96538814323e0609c895

The example is a curated markdown document that catalogs online resources for synthetic media, organized by categories such as synthetic text, voice, music, art, and gaming. It lists specific websites and tools, including thispersondoesnotexist.com, Hugging Face’s BERT, OpenAI’s GPT-3 and Jukebox, Descript, and AI Dungeon, with hyperlinks for each entry. The page is a static text file, not an interactive visualization, using no JavaScript libraries or rendering APIs. It functions as a reference list, showing the breadth of generative media platforms rather than demonstrating any particular code or chart.

55% match

AI for Fashion

This example showcases the intersection of generative AI and fashion through a curated collection of tools and datasets. It presents links to diffusion models like Stable Diffusion and Midjourney, alongside resources such as the Hugging Face `diffusion_fashion` model and the Kaggle Fashion Products dataset. The page uses hyperlinks to connect users directly to these external applications, demonstrating how AI can generate clothing designs from text prompts. The layout is a simple list, relying on the browser’s default anchor styling rather than any custom JavaScript libraries or visualization APIs.

55% match

import seenthis into gensim

This example demonstrates how to transform a dump of the social network Seenthis into a format usable by the gensim topic-modeling library, specifically adapting the Wikipedia-corpus script to generate bag-of-words and TF-IDF vector representations. The code shows a pipeline that reads raw posts, strips markup using regex-based cleaning (including removal of comments, templates, and links), tokenizes the text, and then builds a dictionary and sparse matrix outputs in Matrix Market format. It relies on gensim’s `Dictionary`, `TextCorpus`, and `MmCorpus` classes, plus utility functions like `filter_wiki` for preprocessing, mirroring the original `wikicorpus.py` structure. The output files—a word-ID mapping and two corpus files—allow downstream topic modeling with gensim, though the visualization itself is not shown; the code’s focus is on data preparation rather than graphical display.

PPhilippe Rivière

Gist 34f3e588a35b33357c08853c2370036b

This visualization catalogs AI tools and resources, organizing hundreds of links to libraries and APIs for foundation models, model development, vector databases, and multimodal systems. The layout groups entries by category—such as OpenAI function calling, Microsoft’s BitNet framework, and Hugging Face collections—using a hierarchical list format. It references tools like LM Studio for local deployment, Pyannote for speaker diarization, and Edge-TTS for text-to-speech, with GitHub repositories and documentation pages as primary sources. The rendering relies on standard Markdown headers and hyperlinks to create a browsable, text-based reference.

JJastor11

Gist e71cb443307994e24e63465bac636d3e

This example demonstrates how to extract and summarize the content of a New York Times opinion article about regulating artificial intelligence. The notebook uses the `newspaper` library to download and parse the article, returning its title, publication date, and initial text. It then calls the `nlp()` method to generate a list of keywords and a multi-sentence summary, showing a straightforward pipeline from a URL to condensed article insights. The code relies on `Article` from `newspaper` and standard pandas imports.

GGeorgeMcIntire

Gist d2f2bc6be1716820160ed5183ea07eaa

This example shows a browser-based text analytics pipeline that scrapes and processes web content before visualizing topic models. The code first uses Selenium with Chrome WebDriver to scrape data, then applies NLP libraries including NLTK, spaCy, TextBlob, and gensim’s LdaModel for topic modeling and coherence scoring. The final visualization employs Plotly’s `plotly.express` and `plotly.graph_objs` to render interactive charts, with `pyo.init_notebook_mode()` enabling notebook display. The rendering approach is evident from the use of `go` traces and `px` figures, though the specific chart type is not shown in the code snippet. The data source is dynamic web pages, as indicated by the Selenium scraping logic.

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