# HarderThenHarder/transformers_tasks

⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF, SFT etc.

Repository: https://github.com/HarderThenHarder/transformers_tasks
Canonical: https://ross.abutalabs.com/products/transformers_tasks
Homepage: https://www.zhihu.com/column/c_1451236880973426688
Language: Jupyter Notebook
License Family: other
Topics: nlp, text-classification, text-matching, information-extraction, reinforcement-learning, transformers, text-generation
Last push: 2023-09-29T10:58:39+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 99
- inputs: {"age_days": 1389, "days_push": 1069, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2420, forks 398 (observed 2026-08-28T04:06:50.535113+00:00)

## What it is
A collection of Jupyter Notebook-based NLP algorithm implementations built on the Hugging Face transformers library, covering text classification, text matching, information extraction, prompt tasks, text generation, and RLHF/SFT. It serves as a learning resource and starter codebase where users swap in their own datasets to train task-specific models.

## Use cases
- learn how to fine-tune BERT for text classification
- train a sentence similarity model for search recall
- implement RLHF with a reward model and PPO on GPT2
- do named entity recognition with UIE
- try prompt-based few-shot learning like PET and p-tuning
- fine-tune T5 for Chinese question answering
- learn SFT and LLM alignment from example code

## When to choose
- you want readable, task-by-task example code for transformers-based NLP
- you are learning RLHF, SFT, or prompt tuning with small runnable examples
- you need reference implementations of text matching architectures like DSSM and Sentence-BERT

## When to avoid
- you need a production-ready NLP library with APIs and packaging
- you require a maintained project with a license and active releases
- you need scalable training infrastructure rather than educational scripts

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, llm-training, rag, prompt-engineering
- domain: machine-learning, large-language-models, tutorials
- platform: python, windows
- tags: transformers, huggingface, text-classification, text-matching, information-extraction, rlhf, sft, jupyter-notebooks, chinese-nlp, example-code, natural-language-processing, linux, macos, gpu

## Member repositories
- HarderThenHarder/transformers_tasks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:50.535113+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 2026-08-30T02:33:04.298856+00:00, confidence not recorded.
  - readme: https://github.com/HarderThenHarder/transformers_tasks (fetched 2026-08-28T04:06:50.535113+00:00, sha 2868cfb529a6)
- Data as of 2026-08-30T08:39:29.467469+00:00.