Add new QA eval metric: Semantic Answer Similarity (SAS)

[Timoeller]

SAS is added as part of the Eval Pipeline. 🦜

Intro

The SAS metric correlates better with human judgement of correct answers as it does not rely on string overlaps.

Example:
Prediction = "30%"
Label = "thirty percent"
EM and F1 are both 0, so overly pessimistic
SAS = 0.95 with model "cross-encoder/stsb-roberta-large", which is a much more realistic similarity score.

We showed much higher correlation of SAS score with human judgment in an upcoming paper, link TBD

Tech Details

Bi or Cross-Encoders can both be used. The model can be configured by a model string pointing to SentenceTransformers or HF modelhub models. There currently is no check if the chosen model can be used for SAS, but a decent multilingual default is chosen sentence-transformers/paraphrase-multilingual-mpnet-base-v2

Reader Scores on Tutorial 5 (Evaluation) are:

has answer queries: 24
top 1 EM: 0.1667
top k EM: 0.4583
top 1 F1: 0.3671
top k F1: 0.6425

sentence-transformers/paraphrase-multilingual-mpnet-base-v2
top 1 SAS: 0.6161
top k SAS: 0.7915

cross-encoder/stsb-roberta-large
top 1 SAS: 0.4067
top k SAS: 0.6665

So the scoring also changes depending on the underlying model used. The cross encoder with a large roberta model correlates on average much more with human judgement than the sentence transformers used in this example.

closes #1241

[tholor]

Nice!
I adjusted a few docstrings and namings to be more consistent with the rest of Haystack.
Left a few comments for open points.

[tholor]

LGTM