Add sentence similarity metric using Sentence Transformers

[NISH1001]

Initial code looks like

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

from evalem.nlp.metrics import SemanticMetric
from evalem._base.structures import EvaluationPredictionInstance, EvaluationReferenceInstance
from evalem._base.structures import MetricResult

class SentenceSimilarity(SemanticMetric):
    def __init__(self, model = 'all-MiniLM-L6-v2') -> None:
        self.model = SentenceTransformer(model)
        
    def compute(
        self,
        predictions: EvaluationPredictionInstance,
        references: EvaluationReferenceInstance,
        **kwargs,
    ) -> MetricResult:
        embeddings_preds = self.model.encode(predictions)
        embeddings_refs = self.model.encode(references)
        scores = np.diag(cosine_similarity(embeddings_preds, embeddings_refs))

        # scores = sent_util.cos_sim(embeddings_preds, embeddings_refs)
        return MetricResult(score=np.mean(scores), metric_name="SentenceSimilarity", total_items=len(predictions), extra=dict(scores=scores))

class CrossEncoderSentenceSimilarity(SemanticMetric):
    def __init__(self, model = 'cross-encoder/stsb-distilroberta-base') -> None:
        self.model_name = model
        self.model = CrossEncoder(model)
        
    def compute(
        self,
        predictions: EvaluationPredictionInstance,
        references: EvaluationReferenceInstance,
        **kwargs,
    ) -> MetricResult:
        sentences = list(zip(references, predictions))
        scores = self.model.predict(sentences)

        # scores = sent_util.cos_sim(embeddings_preds, embeddings_refs)
        return MetricResult(
            score=np.mean(scores),
            metric_name="CrossEncoderSentenceSimilarity",
            total_items=len(predictions),
            extra=dict(scores=scores, model=self.model_name)
        )

result = SentenceSimilarity()(
    references=[...], # flattened list
    predictions=[...], # flattened list
) # gives an object of MetricResult

result = CrossEncoderSentenceSimilarity()(
    references=[...], # flattened list
    predictions=[...], # flattened list
) # gives an object of MetricResult