File size: 3,419 Bytes
545c4d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | from typing import Dict
import torch
from cde_benchmark.formatters.data_formatter import BaseDataFormatter
from cde_benchmark.evaluators.eval_utils import CustomRetrievalEvaluator
class Embedder:
def __init__(
self,
is_contextual_model: bool = False,
):
self.is_contextual_model = is_contextual_model
self.evaluator = CustomRetrievalEvaluator()
def embed_queries(self, queries):
raise NotImplementedError
def embed_documents(self, documents):
raise NotImplementedError
def process_queries(self, data_formatter):
queries, document_ids = data_formatter.get_queries()
query_embeddings = self.embed_queries(queries)
# make into a contiguous tensor, and map position to document_ids
return query_embeddings, document_ids
def process_documents(self, data_formatter):
if self.is_contextual_model:
documents, document_ids = data_formatter.get_nested()
# embed documents in contextual models receive a list of list of documents and should return embeddings in the same shape
doc_embeddings = self.embed_documents(documents)
# flatten
document_ids = [id_ for nested_ids in document_ids for id_ in nested_ids]
doc_embeddings = [
embed_ for nested_embeds in doc_embeddings for embed_ in nested_embeds
]
else:
documents, document_ids = data_formatter.get_flattened()
doc_embeddings = self.embed_documents(documents)
# make into a contiguous tensor, and map position to document_ids
return doc_embeddings, document_ids
def get_similarities(self, query_embeddings, doc_embeddings):
# convert to torch tensors and compute similarity with dot product
query_embeddings = torch.tensor(query_embeddings)
doc_embeddings = torch.tensor(doc_embeddings)
scores = torch.mm(query_embeddings, doc_embeddings.t())
return scores
def get_metrics(self, scores, all_document_ids, label_documents_id):
# scores are a list of list of scores (or 2D tensor)
# label_document_ids are a list of document ids corresponding to the true label
# all_document_ids are a list of all document ids in the same order as the scores
assert scores.shape[1] == len(all_document_ids)
assert scores.shape[0] == len(label_documents_id)
assert set(label_documents_id).issubset(set(all_document_ids))
relevant_docs = {}
for idx, label in enumerate(label_documents_id):
relevant_docs[str(idx)] = {label: 1}
results = {}
for idx, scores_per_query in enumerate(scores):
results[str(idx)] = {
str(doc_id): score.item()
for doc_id, score in zip(all_document_ids, scores_per_query)
}
metrics: Dict[str, float] = self.evaluator.compute_mteb_metrics(
relevant_docs, results
)
return metrics
def compute_metrics_e2e(self, data_formatter):
queries_embeddings, label_ids = self.process_queries(data_formatter)
documents_embeddings, all_doc_ids = self.process_documents(data_formatter)
scores = self.get_similarities(queries_embeddings, documents_embeddings)
metrics = self.get_metrics(scores, all_doc_ids, label_ids)
return metrics
|