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correct return type for encoding #1119

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correct return type for encoding #1119

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14 changes: 7 additions & 7 deletions FlagEmbedding/flag_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -237,7 +237,7 @@ def __init__(
def encode_queries(self, queries: Union[List[str], str],
batch_size: int = 256,
max_length: int = 512,
convert_to_numpy: bool = True) -> np.ndarray:
convert_to_numpy: bool = True) -> Union[np.ndarray, torch.Tensor]:
'''
This function will be used for retrieval task
if there is a instruction for queries, we will add it to the query text
Expand All @@ -252,7 +252,7 @@ def encode_corpus(self,
corpus: Union[List[str], str],
batch_size: int = 256,
max_length: int = 512,
convert_to_numpy: bool = True) -> np.ndarray:
convert_to_numpy: bool = True) -> Union[np.ndarray, torch.Tensor]:
'''
This function will be used for retrieval task
encode corpus for retrieval task
Expand All @@ -264,7 +264,7 @@ def encode(self,
sentences: Union[List[str], str],
batch_size: int = 256,
max_length: int = 512,
convert_to_numpy: bool = True) -> np.ndarray:
convert_to_numpy: bool = True) -> Union[np.ndarray, torch.Tensor]:
if self.num_gpus > 0:
batch_size = batch_size * self.num_gpus
self.model.eval()
Expand Down Expand Up @@ -341,7 +341,7 @@ def __init__(
def encode_queries(self, queries: Union[List[str], str],
batch_size: int = 256,
max_length: int = 512,
convert_to_numpy: bool = True) -> np.ndarray:
convert_to_numpy: bool = True) -> Union[np.ndarray, torch.Tensor]:
'''
This function will be used for retrieval task
if there is a instruction for queries, we will add it to the query text
Expand All @@ -350,7 +350,7 @@ def encode_queries(self, queries: Union[List[str], str],
if isinstance(queries, str):
input_texts = self.query_instruction_for_retrieval + queries
else:
input_texts = ['{}{}'.format(self.query_instruction_for_retrieval, q) for q in queries]
input_texts = [(self.query_instruction_for_retrieval + q) for q in queries]
else:
input_texts = queries
return self.encode(input_texts, batch_size=batch_size, max_length=max_length, convert_to_numpy=convert_to_numpy)
Expand All @@ -359,7 +359,7 @@ def encode_corpus(self,
corpus: Union[List[str], str],
batch_size: int = 256,
max_length: int = 512,
convert_to_numpy: bool = True) -> np.ndarray:
convert_to_numpy: bool = True) -> Union[np.ndarray, torch.Tensor]:
'''
This function will be used for retrieval task
encode corpus for retrieval task
Expand All @@ -371,7 +371,7 @@ def encode(self,
sentences: Union[List[str], str],
batch_size: int = 256,
max_length: int = 512,
convert_to_numpy: bool = True) -> np.ndarray:
convert_to_numpy: bool = True) -> Union[np.ndarray, torch.Tensor]:
if self.num_gpus > 0:
batch_size = batch_size * self.num_gpus
self.model.eval()
Expand Down
2 changes: 1 addition & 1 deletion Tutorials/1_Embedding/1.2.1_BGE_Series.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -266,7 +266,7 @@
"```\n",
"LLMEmbedder.encode_keys(keys, batch_size=256, max_length=512, task='qa')\n",
"```\n",
"Similarly, *encode_keys()* also calls *_encode()* and automatically add instructions according to given task."
"Similarly, *encode_keys()* also calls *_encode()* and automatically add instructions according to the given task."
]
},
{
Expand Down