noindex

Author: ZiyiXia

FlagEmbedding/abc/inference/AbsEmbedder.py

@@ -33,8 +33,6 @@ class AbsEmbedder(ABC):
         batch_size (int, optional): Batch size for inference. Defaults to :data:`256`.
         query_max_length (int, optional): Maximum length for query. Defaults to :data:`512`.
         passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.
-        instruction (Optional[str], optional): Instruction for embedding with :attr:`instruction_format`. Defaults to :data:`None`.
-        instruction_format (str, optional): Instruction format when using :attr:`instruction`. Defaults to :data:`"{}{}"`.
         convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor. 
             Defaults to :data:`True`.
         kwargs (Dict[Any], optional): Additional parameters for HuggingFace Transformers config or children classes.
@@ -139,10 +137,10 @@ def encode_queries(
 
         Args:
             queries (Union[List[str], str]): Input queries to encode.
-            batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to None.
-            max_length (Optional[int], optional): Maximum length of tokens. Defaults to None.
+            batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.
+            max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.
             convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will 
-                be a Torch Tensor. Defaults to None.
+                be a Torch Tensor. Defaults to :data:`None`.
 
         Returns:
             Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.
@@ -173,10 +171,10 @@ def encode_corpus(
 
         Args:
             corpus (Union[List[str], str]): Input corpus to encode.
-            batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to None.
-            max_length (Optional[int], optional): Maximum length of tokens. Defaults to None.
+            batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.
+            max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.
             convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will 
-                be a Torch Tensor. Defaults to None.
+                be a Torch Tensor. Defaults to :data:`None`.
 
         Returns:
             Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.
@@ -212,12 +210,12 @@ def encode(
 
         Args:
             sentences (Union[List[str], str]): Input sentences to encode.
-            batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to None.
-            max_length (Optional[int], optional): Maximum length of tokens. Defaults to None.
+            batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.
+            max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.
             convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will 
-                be a Torch Tensor. Defaults to None.
-            instruction (Optional[str], optional): The text of instruction. Defaults to None.
-            instruction_format (Optional[str], optional): Format for instruction. Defaults to None.
+                be a Torch Tensor. Defaults to :data:`None`.
+            instruction (Optional[str], optional): The text of instruction. Defaults to :data:`None`.
+            instruction_format (Optional[str], optional): Format for instruction. Defaults to :data:`None`.
 
         Returns:
             Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.

FlagEmbedding/abc/inference/AbsReranker.py

@@ -28,12 +28,12 @@ class AbsReranker(ABC):
             with :attr:`query_instruction_format`. Defaults to :data:`None`.
         query_instruction_format: (str, optional): The template for :attr:`query_instruction_for_rerank`. Defaults to :data:`"{}{}"`.
         passage_instruction_for_rerank (Optional[str], optional): Passage instruction for reranking. Defaults to :data:`None`.
-        passage_instruction_format (str, optional): Passage instruction format when using `passage_instruction_for_rerank`. 
+        passage_instruction_format (str, optional): Passage instruction format when using :attr:`passage_instruction_for_rerank`. 
             Defaults to :data:`"{}{}"`.
         devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.
         batch_size (int, optional): Batch size for inference. Defaults to :data:`128`.
         query_max_length (int, optional): Maximum length for query. Defaults to :data:`None`.
-        passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.
+        max_length (int, optional): Maximum length. Defaults to :data:`512`.
         normalize (bool, optional): If true, normalize the result. Defaults to :data:`False`.
         kwargs (Dict[Any], optional): Additional parameters for HuggingFace Transformers config or children classes.
     """

FlagEmbedding/inference/auto_embedder.py

@@ -55,7 +55,7 @@ def from_finetuned(
             ValueError
 
         Returns:
-            AbsEmbedder: The model class to load model, which is child class of :clsss:`AbsEmbedder`.
+            AbsEmbedder: The model class to load model, which is child class of :class:`AbsEmbedder`.
         """
         model_name = os.path.basename(model_name_or_path)
         if model_name.startswith("checkpoint-"):

FlagEmbedding/inference/auto_reranker.py

@@ -44,7 +44,7 @@ def from_finetuned(
             ValueError
 
         Returns:
-            AbsReranker: The reranker class to load model, which is child class of :clsss:`AbsReranker`.
+            AbsReranker: The reranker class to load model, which is child class of :class:`AbsReranker`.
         """
         model_name = os.path.basename(model_name_or_path)
         if model_name.startswith("checkpoint-"):