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‎.github/workflows/build_documentation.yml‎

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package: course
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path_to_docs: course/chapters/
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additional_args: --not_python_module
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languages: ar bn de en es fa fr gj he hi id it ja ko ne pt ru rum th tr vi zh-CN zh-TW
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languages: ar bn de en es fa fr gj he hi id it ja ko my ne pl pt ru ro te th tr vi zh-CN zh-TW
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secrets:
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hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

‎.github/workflows/build_pr_documentation.yml‎

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package: course
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path_to_docs: course/chapters/
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additional_args: --not_python_module
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languages: ar bn de en es fa fr gj he hi id it ja ko ne pt ru rum th tr vi zh-CN zh-TW
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languages: ar bn de en es fa fr gj he hi id it ja ko my ne pl pt ru ro te th tr vi zh-CN zh-TW

‎README.md‎

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| [Korean](https://huggingface.co/course/ko/chapter1/1) (WIP) | [`chapters/ko`](https://github.com/huggingface/course/tree/main/chapters/ko) | [@Doohae](https://github.com/Doohae), [@wonhyeongseo](https://github.com/wonhyeongseo), [@dlfrnaos19](https://github.com/dlfrnaos19), [@nsbg](https://github.com/nsbg) |
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| [Portuguese](https://huggingface.co/course/pt/chapter1/1) (WIP) | [`chapters/pt`](https://github.com/huggingface/course/tree/main/chapters/pt) | [@johnnv1](https://github.com/johnnv1), [@victorescosta](https://github.com/victorescosta), [@LincolnVS](https://github.com/LincolnVS) |
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| [Russian](https://huggingface.co/course/ru/chapter1/1) (WIP) | [`chapters/ru`](https://github.com/huggingface/course/tree/main/chapters/ru) | [@pdumin](https://github.com/pdumin), [@svv73](https://github.com/svv73), [@blademoon](https://github.com/blademoon) |
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| [Telugu]( https://huggingface.co/course/te/chapter0/1 ) (WIP) | [`chapters/te`](https://github.com/huggingface/course/tree/main/chapters/te) | [@Ajey95](https://github.com/Ajey95), [@RahulKonda18](https://github.com/RahulKonda18)
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| [Thai](https://huggingface.co/course/th/chapter1/1) (WIP) | [`chapters/th`](https://github.com/huggingface/course/tree/main/chapters/th) | [@peeraponw](https://github.com/peeraponw), [@a-krirk](https://github.com/a-krirk), [@jomariya23156](https://github.com/jomariya23156), [@ckingkan](https://github.com/ckingkan) |
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| [Turkish](https://huggingface.co/course/tr/chapter1/1) (WIP) | [`chapters/tr`](https://github.com/huggingface/course/tree/main/chapters/tr) | [@tanersekmen](https://github.com/tanersekmen), [@mertbozkir](https://github.com/mertbozkir), [@ftarlaci](https://github.com/ftarlaci), [@akkasayaz](https://github.com/akkasayaz) |
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| [Vietnamese](https://huggingface.co/course/vi/chapter1/1) | [`chapters/vi`](https://github.com/huggingface/course/tree/main/chapters/vi) | [@honghanhh](https://github.com/honghanhh) |
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| [Chinese (simplified)](https://huggingface.co/course/zh-CN/chapter1/1) | [`chapters/zh-CN`](https://github.com/huggingface/course/tree/main/chapters/zh-CN) | [@zhlhyx](https://github.com/zhlhyx), [petrichor1122](https://github.com/petrichor1122), [@1375626371](https://github.com/1375626371) |
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| [Chinese (traditional)](https://huggingface.co/course/zh-TW/chapter1/1) (WIP) | [`chapters/zh-TW`](https://github.com/huggingface/course/tree/main/chapters/zh-TW) | [@davidpeng86](https://github.com/davidpeng86) |
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| [Chinese (traditional)](https://huggingface.co/course/zh-TW/chapter1/1) (WIP) | [`chapters/zh-TW`](https://github.com/huggingface/course/tree/main/chapters/zh-TW) | [@davidpeng86](https://github.com/davidpeng86), [@thliang01](https://github.com/thliang01) |
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| [Romanian](https://huggingface.co/course/ro/chapter1/1) (WIP) | [`chapters/ro`](https://github.com/huggingface/course/tree/main/chapters/ro) | [@Sigmoid](https://github.com/SigmoidAI), [@eduard-balamatiuc](https://github.com/eduard-balamatiuc), [@FriptuLudmila](https://github.com/FriptuLudmila), [@tokyo-s](https://github.com/tokyo-s), [@hbkdesign](https://github.com/hbkdesign), [@grumpycatyo-collab](https://github.com/grumpycatyo-collab), [@Angroys](https://github.com/Angroys) |
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### Translating the course into your language
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‎chapters/de/chapter3/2.mdx‎

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```python
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import torch
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from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification
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from torch.optim import AdamW
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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# Genau wie vorher
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checkpoint = "bert-base-uncased"

‎chapters/de/chapter3/4.mdx‎

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Wir sind fast so weit, unsere Trainingsschleife zu schreiben! Es fehlen nur noch zwei Dinge: ein Optimierer und ein Scheduler für die Lernrate. Da wir versuchen, das zu wiederholen, was der `Trainer` automatisch gemacht hat, werden wir die gleichen Standardwerte verwenden. Der Optimierer, den der `Trainer` verwendet, heißt "AdamW" und ist größtenteils derselbe wie Adam, abgesehen von einer Abwandlung für die "Weight Decay Regularization" (siehe ["Decoupled Weight Decay Regularization"] (https://arxiv.org/abs/1711.05101) von Ilya Loshchilov und Frank Hutter):
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```py
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from transformers import AdamW
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from torch.optim import AdamW
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optimizer = AdamW(model.parameters(), lr=5e-5)
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```
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Die Trainingsschleife, die wir zuvor definiert haben, funktioniert gut auf einer einzelnen CPU oder GPU. Aber mit der Bibliothek [🤗 Accelerate](https://github.com/huggingface/accelerate) können wir mit wenigen Anpassungen verteiltes Training auf mehreren GPUs oder TPUs implementieren. Beginnend mit der Erstellung der Trainings- und Validierungsdaten, sieht unsere manuelle Trainingsschleife nun folgendermaßen aus:
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from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
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from torch.optim import AdamW
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from transformers import AutoModelForSequenceClassification, get_scheduler
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optimizer = AdamW(model.parameters(), lr=3e-5)
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```diff
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+ from accelerate import Accelerator
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‎chapters/en/_toctree.yml‎

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- local: chapter1/1
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title: Introduction
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title: Natural Language Processing
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title: Natural Language Processing and Large Language Models
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- local: chapter1/3
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title: Transformers, what can they do?
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- local: chapter1/4
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title: How do Transformers work?
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- local: chapter1/5
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title: Encoder models
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title: How 🤗 Transformers solve tasks
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- local: chapter1/6
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title: Transformer Architectures
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title: Sequence-to-sequence models
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title: Quick quiz
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- local: chapter1/8
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title: Bias and limitations
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title: Inference with LLMs
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- local: chapter1/9
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title: Summary
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title: Bias and limitations
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- local: chapter1/10
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title: Summary
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- local: chapter1/11
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title: Certification exam
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- title: 2. Using 🤗 Transformers
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title: Basic usage completed!
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title: Optimized Inference Deployment
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- local: chapter2/9
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title: Processing the data
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- local: chapter3/3
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title: Fine-tuning a model with the Trainer API or Keras
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title: Fine-tuning a model with the Trainer API
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title: A full training
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title: A full training loop
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title: Understanding Learning Curves
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title: Fine-tuning, Check!
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- title: 7. Main NLP tasks
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- title: 7. Classical NLP tasks
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sections:
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title: Introduction
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title: Question answering
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title: Mastering NLP
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title: Mastering LLMs
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title: Reinforcement Learning on LLMs
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- local: chapter12/3
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title: The Aha Moment in the DeepSeek R1 Paper
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- local: chapter12/3a
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title: Advanced Understanding of GRPO in DeepSeekMath
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title: Implementing GRPO in TRL
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title: Practical Exercise with Unsloth
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- local: chapter12/7
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title: Coming soon...
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- title: Course Events

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