# ashishpatel26/Treasure-of-Transformers

💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️

Repository: https://github.com/ashishpatel26/Treasure-of-Transformers
Canonical: https://ross.abutalabs.com/products/treasure-of-transformers
Homepage: https://github.com/ashishpatel26/Treasure-of-Transformers.git
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: transformer, python, nlp, natural-language-processing, tensorflow, pytorch, speech-recognition, seq2seq, pretrained-models, language-models, natural-language-generation, nlp-library, bert, natural-language-understanding, language-model, pytorch-transformers, model-hub, jax, awesome
Last push: 2025-08-01T12:14:19+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 34, release rhythm 35, longevity 100
- inputs: {"age_days": 1724, "days_push": 397, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1179, forks 237 (observed 2026-08-28T04:03:53.132373+00:00)

## What it is
A curated awesome-list of transformer models for NLP, linking papers, blogs, videos, official repositories, and Colab notebooks for each model (BERT, GPT, T5, etc.). It is a reference resource rather than runnable software.

## Use cases
- find papers and code for transformer models
- learn how BERT or GPT works with notebooks
- compare transformer architectures by year
- find colab notebooks for NLP models
- discover official repos for pretrained language models
- study seq2seq and attention models

## When to choose
- you want a curated index of transformer papers, videos, and code
- you are learning NLP deep learning models and want runnable notebooks
- you need quick links to official implementations of models like T5, BART, or Longformer

## When to avoid
- you need a production NLP library or inference runtime
- you want maintained code rather than a link collection
- you need up-to-date coverage of the newest LLMs

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning, documentation
- domain: deep-learning, machine-learning, tutorials, awesome-lists
- platform: python, cross-platform
- tags: awesome-list, transformers, curated-resources, colab-notebooks, pretrained-models, papers, speech-recognition, natural-language-processing

## Member repositories
- ashishpatel26/Treasure-of-Transformers (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:53.132373+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:25:31.192202+00:00, confidence not recorded.
  - readme: https://github.com/ashishpatel26/Treasure-of-Transformers (fetched 2026-08-28T04:03:53.132373+00:00, sha 46b4a8761be8)
  - homepage: https://github.com/ashishpatel26/Treasure-of-Transformers.git (fetched 2026-08-29T12:32:40.499075+00:00, sha bc721ec2a994)
- Data as of 2026-08-30T08:39:29.467469+00:00.