# jsksxs360/How-to-use-Transformers

Transformers 库快速入门教程

Repository: https://github.com/jsksxs360/How-to-use-Transformers
Canonical: https://ross.abutalabs.com/products/how-to-use-transformers
Homepage: https://transformers.run/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: nlp, pytorch, transformers, bert, classification, natural-language-processing, ner, prompt, qa, sentiment-classification, summarization, transformer, translation
Last push: 2026-02-24T06:26:48+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 69, release rhythm 35, longevity 100
- inputs: {"age_days": 1444, "days_push": 190, "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 1891, forks 227 (observed 2026-08-28T04:05:49.628077+00:00)

## What it is
A Chinese-language tutorial and code repository for quickly learning the Hugging Face Transformers library, covering NLP fundamentals through fine-tuning and large language models. It accompanies the transformers.run book with runnable example code for tasks like classification, NER, translation, summarization, and QA.

## Use cases
- learn how to use the huggingface transformers library
- fine-tune bert for text classification
- build a named entity recognition model with transformers
- translate text with seq2seq models
- do extractive question answering with transformers
- learn prompt-based sentiment analysis
- get started with large language models and instruction tuning

## When to choose
- you are an NLP beginner wanting a structured Transformers tutorial
- you prefer learning from runnable task-specific example code
- you want Chinese-language explanations of Transformers and LLMs

## When to avoid
- you need production-ready NLP pipelines rather than educational examples
- you need multilingual or multimodal coverage
- you already know Transformers and only need API reference

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, machine-learning, deep-learning, transformers
- domain: machine-learning, tutorials, large-language-models
- platform: python
- tags: huggingface-transformers, tutorial, pytorch, bert, fine-tuning, chinese, example-code, natural-language-processing

## Member repositories
- jsksxs360/How-to-use-Transformers (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:49.628077+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 2026-08-30T03:13:00.296582+00:00, confidence not recorded.
  - readme: https://github.com/jsksxs360/How-to-use-Transformers (fetched 2026-08-28T04:05:49.628077+00:00, sha edd036bdda80)
  - homepage: https://transformers.run/ (fetched 2026-08-29T10:52:10.407043+00:00, sha 7e3af9ab67b7)
- Data as of 2026-08-30T08:39:29.467469+00:00.