Text-Summarization-Models
This repository contains various models for text summarization tasks. Each model has a separate directory containing the implementation code, pretrained weights, and a Jupyter notebook for testing the model on sample input texts. Feel free to use these models for your own text summarization tasks or to experiment with them further
What XERJ indexed
Selected work
Licence
The repository
LICENSE file checked at the pinned commit; the author agreed to inclusion.
The XERJ corpus
The owner added it on hub.xerj.org after signing in with GitHub, on 2026-10-29. Same terms as the repository. Answers name the repo, the commit and the file:line.
selected for the XERJ Corpus Hublicence clearedsuggested use: adapt with attribution
bertbartgraph-summarizationlexrankpegasussumyt5textrank
Example questions
- How does Text-Summarization-Models implement bert?
- Show the bart code in Text-Summarization-Models.
Inside the indexed corpus
- Languages by size: Jupyter Notebook 97.3%, Python 2.7%.
- On GitHub since 2023-02-23, last push 2026-10-29, 42 commits on main.
Licence on GitHub and in the corpus
GitHub reported no licence for Tuhin-SnapD/Text-Summarization-Models on 2026-10-29. At commit 040b069135 the LICENSE file was reviewed as NONE.
Contributors
Tuhin Chakrabarty (@Tuhin-SnapD) has 48 selected repositories on the hub.
Their other repository pages: Tuhin-SnapD/NewsMelt-Advance-News-Finder, Tuhin-SnapD/Online-Banking-App, Tuhin-SnapD/ChatApp, Tuhin-SnapD/Prison-Management-System, Tuhin-SnapD/Real-Time-Stock-Trading-Simulator, Tuhin-SnapD/BinkRead.
Using XERJ with Text-Summarization-Models
- Search Jupyter notebooks (.ipynb) and the .py beside them Jupyter Notebook is 97.3% of the code by size.
- RAG without a vector database: one hybrid index The hub files it under AI, prompts and agents.
- Ask questions about a GitHub repo without cloning it
- Index a reference library for a coding agent
The prompt
Paste it into your AI agent. It clones Tuhin-SnapD/Text-Summarization-Models at the pinned commit and indexes it on your machine.
Search the GitHub repository Tuhin-SnapD/Text-Summarization-Models with XERJ (https://hub.xerj.org/corpus/tuhin-snapd--text-summarization-models). Run these in a shell, in order, and check each result before the next: 1. xerj --help (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh 2. git clone https://github.com/Tuhin-SnapD/Text-Summarization-Models Text-Summarization-Models && git -C Text-Summarization-Models checkout 040b06913548bfbf79138978ac692628ffcb0253 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./Text-Summarization-Models --url http://127.0.0.1:9200 --prefix text-summarization-model --progress plain 5. xerj autoindex map --url http://127.0.0.1:9200 Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt Licence: check the LICENSE file. Keep the copyright notice and credit the authors.
Related
Tuhin ChakrabartyAI, prompts and agents corporakubernetes-communitycovid-qagemini-cookbookJohnMachado11/GPT-2s0cks/prose2jsonelixir-nx/bumblebee
Stats: GitHub API, fetched 2026-10-29. Selected work and consent: the XERJ registry.