[FR] Continued proofreading and visual corrections #167
Conversation
- review chapter 5, 8 & event - visual fix (italic in # lines) on chapter 0 to 4
the book is now available
fix tag
the comma 🙃
It was not the comma... I don't know what the problem is. Probably HTML tags to generate a link. So I'll remove them, the user will just have to copy/paste the link in parenthesis
|
The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. |
fix italic
fix italic
fix italic
use better word for "check"
little fix
visual fix
visual fix
little fix
fix italic
code_formatter.py
|
The HuggingFaceDocBuilderDev allowed me to view my corrections and realize that some italic tags were missing. |
review chapter 6
fix link
fix link
fix AlBERT
Add chapter 9
add chapter 9 to toctree
There was a problem hiding this comment.
Thank you for another epic PR @lbourdois 🤯 !!
There's a few merge conflicts to resolve from some PRs I just merged, but after they're resolved I think this is good to go 🚀
| @@ -162,7 +162,7 @@ This time, it will report the validation loss and metrics at the end of each epo | |||
|
|
|||
| The `Trainer` will work out of the box on multiple GPUs or TPUs and provides lots of options, like mixed-precision training (use `fp16 = True` in your training arguments). We will go over everything it supports in Chapter 10. | |||
|
|
|||
| This concludes the introduction to fine-tuning using the `Trainer` API. An example of doing this for most common NLP tasks will be given in [Chapter 7](course/chapter7), but for now let's look at how to do the same thing in pure PyTorch. | |||
| This concludes the introduction to fine-tuning using the `Trainer` API. An example of doing this for most common NLP tasks will be given in [Chapter 7](/course/chapter7), but for now let's look at how to do the same thing in pure PyTorch. | |||
|
|
||
| La plupart du cours repose sur le fait que vous ayez un compte Hugging Face. Si vous n'en disposez pas d'un, nous vous recommandons d'en créer un dès maintenant : [créer un compte](https://huggingface.co/join). | ||
|
|
||
| ## Utilisation d'un <i>notebook</i> Google Colab |
| @@ -96,13 +96,15 @@ from transformers import pipeline | |||
|
|
|||
| classifier = pipeline("zero-shot-classification") | |||
| classifier( | |||
| "This is a course about the Transformers library", # C'est un cours sur la bibliothèque Transformers | |||
| "This is a course about the Transformers library", | |||
| # C'est un cours sur la bibliothèque Transformers | |||
There was a problem hiding this comment.
This is a really cool example to show readers what the text is in their native tongue - nice idea!
Since French has reasonable coverage model-wise, one idea for the future would be to change the code so that we use French models instead of English ones. This will require some work on our side to make sure the Colab notebooks are generated per language, but could be interesting to explore in the future
There was a problem hiding this comment.
This is something I had in mind. I'll submit a list of ideas to you on Discord.
Hi,
In this PR:
The only thing missing is the reviewing of chapters 7 (and maybe chapter 9 visually) and the translation of the videos for the French version to be finished :D