Add FAQ (#354)

chapters/en/chapter1/1.mdx

@@ -55,7 +55,54 @@ About the authors:
 
 **Leandro von Werra**  is a machine learning engineer in the open-source team at Hugging Face and also a co-author of the O’Reilly book [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/). He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack..
 
+## FAQ
+
+Here are some answers to frequently asked questions:
+
+- **Does taking this course lead to a certification?**
+Currently we do not have any certification for this course. However, we are working on a certification program for the Hugging Face ecosystem -- stay tuned!
+
+- **How much time should I spend on this course?**
+Each chapter in this course is designed to be completed in 1 week, with approximately 6-8 hours of work per week. However, you can take as much time as you need to complete the course.
+
+- **Where can I ask a question if I have one?**
+If you have a question about any section of the course, just click on the "*Ask a question*" banner at the top of the page to be automatically redirected to the right section of the [Hugging Face forums](https://discuss.huggingface.co/):
+
+<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/forum-button.png" alt="Link to the Hugging Face forums" width="75%">
+
+Note that a list of [project ideas](https://discuss.huggingface.co/c/course/course-event/25) is also available on the forums if you wish to practice more once you have completed the course.
+
+- **Where can I get the code for the course?**
+For each section, click on the banner at the top of the page to run the code in either Google Colab or Amazon SageMaker Studio Lab:
+
+<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/notebook-buttons.png" alt="Link to the Hugging Face course notebooks" width="75%">
+
+The Jupyter notebooks containing all the code from the course are hosted on the [`huggingface/notebooks`](https://github.com/huggingface/notebooks) repo. If you wish to generate them locally, check out the instructions in the [`course`](https://github.com/huggingface/course#-jupyter-notebooks) repo on GitHub.
+
+
+- **How can I contribute to the course?**
+There are many ways to contribute to the course! If you find a typo or a bug, please open an issue on the [`course`](https://github.com/huggingface/course) repo. If you would like to help translate the course into your native language, check out the instructions [here](https://github.com/huggingface/course#translating-the-course-into-your-language).
+
+- ** What were the choices made for the each translation?**
+Each translation has a glossary and `TRANSLATING.txt` file that details the choices that were made for machine learning jargon etc. You can find an example for German [here](https://github.com/huggingface/course/blob/main/chapters/de/TRANSLATING.txt).
+
+
+- **Can I reuse this course?**
+Of course! The course is released under the permissive [Apache 2 license](https://www.apache.org/licenses/LICENSE-2.0.html). This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. If you would like to cite the course, please use the following BibTeX:
+
+```
+@misc{huggingfacecourse,
+  author = {Hugging Face},
+  title = {The Hugging Face Course, 2022},
+  howpublished = "\url{https://huggingface.co/course}",
+  year = {2022},
+  note = "[Online; accessed <today>]"
+}
+```
+
 Are you ready to roll? In this chapter, you will learn:
+
 * How to use the `pipeline()` function to solve NLP tasks such as text generation and classification
 * About the Transformer architecture
 * How to distinguish between encoder, decoder, and encoder-decoder architectures and use cases
+

utils/generate_notebooks.py

@@ -21,6 +21,9 @@
 frameworks = {"pt": "PyTorch", "tf": "TensorFlow"}
 
 PATH_TO_COURSE = Path("chapters/")
+# Languages to exlude from the notebook generation because the notebooks were
+# created manually
+LANGS_TO_EXCLUDE = ["fr"]
 
 
 def read_and_split_frameworks(fname):
@@ -277,6 +280,8 @@ def create_notebooks(language, output_dir):
     languages = [f.stem for f in PATH_TO_COURSE.iterdir() if f.is_dir()]
 
     for language in languages:
+        if language in LANGS_TO_EXCLUDE:
+            continue
         language_output_dir = f"{args.output_dir}/{language}"
         create_notebooks(language, language_output_dir)
         # Remove empty notebook folders