Sorting FAISS scores for similarity search  
### System Info

- `transformers` version: 4.29.2
- Platform: Linux-5.10.157-139.675.amzn2.x86_64-x86_64-with-glibc2.26
- Python version: 3.9.15
- Huggingface_hub version: 0.15.1
- Safetensors version: not installed
- PyTorch version (GPU?): 2.0.1+cu117 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help?

@sgugger, @stevhliu, @MKhalusova 

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```
import pandas as pd

samples_df = pd.DataFrame.from_dict(samples)
samples_df["scores"] = scores
samples_df.sort_values("scores", ascending=False, inplace=True)
```

Please refer [https://huggingface.co/learn/nlp-course/chapter5/6?fw=pt#using-faiss-for-efficient-similarity-search](https://huggingface.co/learn/nlp-course/chapter5/6?fw=pt#using-faiss-for-efficient-similarity-search)

### Expected behavior

I think the sorting of scores should be in ascending and not descending; because the default index is IndexFlatL2 which is L2/Euclidean distance.

It will be great if these two changes are made to relevant documentation

1. Change `ascending=False` to `ascending=True` 
2. There can be a reference that the default scores returned is the Euclidean distances (by digging sourcecode, I understood it's IndexFlatL2), but it will be easy to include this in documentation

**Refer:** [https://discuss.huggingface.co/t/chapter-5-questions/11744/58?u=namburisrinath](https://discuss.huggingface.co/t/chapter-5-questions/11744/58?u=namburisrinath)

**P.S:** I am sorry if this is the correct place to create the bug as the documentation needs to be changed accordingly. People consume Huggingface documentation a lot, so it needs to be fool-proof, so please correct if I am wrong! 
Hi! Thank you for opening the issue.

I tag someone in the team on the course chapter discussion page. Let's wait a reply first 🤗 .