Dear TF Hub Team,

USE paper Section 5 has a interesting paragraph on evaluation where authors use Arc Cosine (Cos Inverse) whose range is 0 to Pi in radians instead of plain cosine distance with range 0 to 2.

". For the pairwise semantic similarity task, we directly assess
the similarity of the sentence embedding produced by our two encoders. As show

I tried building the docs, but was met with a graphviz error. Typically this means I can spend a few hours pecking away at the dependencies until I get stable build... or someone that has it working can export their environment, and publish an environment.yml that we can use with the build instructions.
I was going off of the d2l book since that's a dep here, but their [environment.yml](https://g

I'm playing around with this wonderful code but I'm running into a curious issue when I try to train the model with my own data.

I replicated the personachat_self_original.json file structure and added my own data. I deleted dataset_cache_OpenAIGPTTokenizer file but when I try to train, I get this error:

INFO:train.py:Pad inputs and convert to Tensor
Traceback (most recent call last)

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

Hi,
When we try to tokenize the following sentence:

If we use spacy

a = spacy.load('en_core_web_lg')

doc = a("I like the link http://www.idph.iowa.gov/ohds/oral-health-center/coordinator")

list(doc)

We got

[I, like, the, link, http://www.idph.iowa.gov, /, ohds, /, oral, -, health, -, center, /, coordinator]

But if we use the Spacy transformer tokenizer:

Per this comment in #12

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(