[feat] Integrate NanoBeIR datasets

[ArthurCamara]

As discussed in #2848 (comment), This PR adds a new Evaluator based on the NanoBEIR collection of datasets.

It creates one InformationRetrievalEvaluator for each dataset, and aggregates the results accordingly.

Example:

from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import NanoBEIREvaluator

# Load a model
model = SentenceTransformer('all-mpnet-base-v2')

datasets = ["QuoraRetrieval", "MSMARCO"]
query_prompts = {
"QuoraRetrieval": "Instruct: Given a question, retrieve questions that are semantically equivalent to the given question\nQuery: ",
"MSMARCO": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: "
}

evaluator = NanoBEIREvaluator(
dataset_names=datasets,
name="NanoBEIR",
query_prompts=query_prompts,
)

results = evaluator(model)
'''
NanoBEeIR Evaluation of the model on ['QuoraRetrieval', 'MSMARCO'] dataset:
Evaluating NanoBeIRNanoQuoraRetrieval
Evaluating NanoBeIRNanoMSMARCO

Average Queries: 50.0
Average Corpus: 5044.5

Aggregated for Score Function: cosine
Accuracy@1: 39.00%
Accuracy@3: 57.00%
Accuracy@5: 66.00%
Accuracy@10: 77.00%
Precision@1: 39.00%
Recall@1: 34.03%
Precision@3: 20.67%
Recall@3: 54.07%
Precision@5: 15.00%
Recall@5: 64.27%
Precision@10: 8.90%
Recall@10: 75.97%
MRR@10: 0.5004
NDCG@10: 0.5513
Aggregated for Score Function: dot
Accuracy@1: 39.00%
Accuracy@3: 57.00%
Accuracy@5: 66.00%
Accuracy@10: 77.00%
Precision@1: 39.00%
Recall@1: 34.03%
Precision@3: 20.67%
Recall@3: 54.07%
Precision@5: 15.00%
Recall@5: 64.27%
Precision@10: 8.90%
Recall@10: 75.97%
MRR@10: 0.5004
NDCG@10: 0.5513
'''
logger.info(evaluator.primary_metric)
# => "cosine_ndcg@10"
logger.info(results["mean"][evaluator.primary_metric])
# => 0.5512516989358924

(Note that this depends on #2951)