Description

Add a ReadMe file in the GitHub folder.
Explain usage of the Templates

Other Comments

Principles of NLP Documentation
Each landing page at the folder level should have a ReadMe which explains -
○ Summary of what this folder offers.
○ Why and how it benefits users
○ As applicable - Documentation of using it, brief description etc
Scenarios folder:
○

Please can you train ghostnet.
(i don't have the imagenet dataset)

🚀 Feature

Add information about debugging segmentation fault to README and docs FAQ.

A lot of people are getting this error. It should be easy to debug this using htop memory output.

Hey! I think it would be useful to have a more detailed explanation about:

1. what the dataset should look like for performing NER, similar to the fine-tuning example. The [NER sample](https://github.com/deepset-ai/FARM/blob/97b0211a37ea7c7d64b4602f0e21b65428b2bd76/t

I think we should give/improve the following points :

1. A high-level overview of the pipeline. How everything works, how each module is articulated with the others, etc ...
2. More details about some mechanisms : I am thinking about user-defined callbacks since I've been working on that. But I'm pretty sure many of you will have other ideas 🙂
3. More explanations about some tricks that have

谢谢分享
@imcaspar 能写一下怎样使用吗?

1. 使用该模型继续进行预训练的步骤
2. 使用该模型进行inference的步骤

谢谢

When using a Finder with a TfidfRetriever (InMemoryDocumentStore) and default TransformersReader all indices and scores are printed (see line 75 in tfidf.py), and there is no meta-data being inserted into the documents which are returned (line 96). I commented out the print call and added the following line to the Document constructor:

meta={'name':self.document_store.get_document_by_id(