fix: update auto_match

README.md

@@ -64,8 +64,8 @@ from retrievals import AutoModelForEmbedding
 
 sentences = ["Hello world", "How are you?"]
 model_name_or_path = "sentence-transformers/all-MiniLM-L6-v2"
-model = AutoModelForEmbedding(model_name_or_path, pooling_method="cls")
-sentence_embeddings = model.encode(sentences)
+model = AutoModelForEmbedding(model_name_or_path, pooling_method="mean", normalize_embeddings=True)
+sentence_embeddings = model.encode(sentences, convert_to_tensor=True)
 print(sentence_embeddings)
 ```
 

src/retrievals/models/embedding_auto.py

@@ -5,7 +5,6 @@
 import numpy as np
 import torch
 from numpy import ndarray
-from peft import LoraConfig, TaskType, get_peft_model
 from torch import Tensor, nn
 from torch.utils.data import DataLoader, Dataset
 from tqdm.autonotebook import trange
@@ -76,7 +75,7 @@ def __init__(
         generation_args: Dict = None,
         use_fp16: bool = False,
         use_lora: bool = False,
-        peft_config: Optional[LoraConfig] = None,
+        peft_config=None,
         device: Optional[str] = None,
         trust_remote_code: bool = False,
     ):
@@ -110,6 +109,8 @@ def __init__(
             self.model.half()
         if use_lora:
             # peft config and wrapping
+            from peft import LoraConfig, TaskType, get_peft_model
+
             if not peft_config:
                 raise ValueError("If use_lora is true, please provide a valid peft_config")
             self.model = get_peft_model(self.model, peft_config)

src/retrievals/models/match_auto.py

@@ -16,7 +16,13 @@ def __init__(self, method="cosine") -> None:
         self.method = method
 
     def similarity_search(
-        self, query_embed: torch.Tensor, passage_embed: torch.Tensor, top_k: int = 1, batch_size: int = 0, **kwargs
+        self,
+        query_embed: torch.Tensor,
+        passage_embed: torch.Tensor,
+        top_k: int = 1,
+        batch_size: int = 0,
+        convert_to_numpy: bool = True,
+        **kwargs,
     ):
         if self.method == "knn":
             neighbors_model = NearestNeighbors(n_neighbors=top_k, metric="cosine", n_jobs=-1)
@@ -25,7 +31,9 @@ def similarity_search(
             return dists, indices
 
         elif self.method == "cosine":
-            dists, indices = cosine_similarity_search(query_embed, passage_embed, top_k=top_k, batch_size=batch_size)
+            dists, indices = cosine_similarity_search(
+                query_embed, passage_embed, top_k=top_k, batch_size=batch_size, convert_to_numpy=convert_to_numpy
+            )
             return dists, indices
 
         else: