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fix: typing issue raising warnings in mypy and pylance #118

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fix: typing issue raising warnings in mypy and pylance #118

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2 changes: 1 addition & 1 deletion beir/retrieval/evaluation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@

class EvaluateRetrieval:

def __init__(self, retriever: Union[Type[DRES], Type[DRFS], Type[BM25], Type[SS]] = None, k_values: List[int] = [1,3,5,10,100,1000], score_function: str = "cos_sim"):
def __init__(self, retriever: Union[DRES, DRFS, BM25, SS] = None, k_values: List[int] = [1,3,5,10,100,1000], score_function: str = "cos_sim"):
self.k_values = k_values
self.top_k = max(k_values)
self.retriever = retriever
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12 changes: 6 additions & 6 deletions beir/retrieval/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,12 +15,12 @@

class TrainRetriever:

def __init__(self, model: Type[SentenceTransformer], batch_size: int = 64):
def __init__(self, model: SentenceTransformer, batch_size: int = 64):
self.model = model
self.batch_size = batch_size

def load_train(self, corpus: Dict[str, Dict[str, str]], queries: Dict[str, str],
qrels: Dict[str, Dict[str, int]]) -> List[Type[InputExample]]:
qrels: Dict[str, Dict[str, int]]) -> List[InputExample]:

query_ids = list(queries.keys())
train_samples = []
Expand All @@ -40,7 +40,7 @@ def load_train(self, corpus: Dict[str, Dict[str, str]], queries: Dict[str, str],
logger.info("Loaded {} training pairs.".format(len(train_samples)))
return train_samples

def load_train_triplets(self, triplets: List[Tuple[str, str, str]]) -> List[Type[InputExample]]:
def load_train_triplets(self, triplets: List[Tuple[str, str, str]]) -> List[InputExample]:

train_samples = []

Expand All @@ -53,15 +53,15 @@ def load_train_triplets(self, triplets: List[Tuple[str, str, str]]) -> List[Type
logger.info("Loaded {} training pairs.".format(len(train_samples)))
return train_samples

def prepare_train(self, train_dataset: List[Type[InputExample]], shuffle: bool = True, dataset_present: bool = False) -> DataLoader:
def prepare_train(self, train_dataset: List[InputExample], shuffle: bool = True, dataset_present: bool = False) -> DataLoader:

if not dataset_present:
train_dataset = SentencesDataset(train_dataset, model=self.model)

train_dataloader = DataLoader(train_dataset, shuffle=shuffle, batch_size=self.batch_size)
return train_dataloader

def prepare_train_triplets(self, train_dataset: List[Type[InputExample]]) -> DataLoader:
def prepare_train_triplets(self, train_dataset: List[InputExample]) -> DataLoader:

train_dataloader = datasets.NoDuplicatesDataLoader(train_dataset, batch_size=self.batch_size)
return train_dataloader
Expand Down Expand Up @@ -117,7 +117,7 @@ def fit(self,
steps_per_epoch = None,
scheduler: str = 'WarmupLinear',
warmup_steps: int = 10000,
optimizer_class: Type[Optimizer] = AdamW,
optimizer_class: Optimizer = AdamW,
optimizer_params : Dict[str, object]= {'lr': 2e-5, 'eps': 1e-6, 'correct_bias': False},
weight_decay: float = 0.01,
evaluation_steps: int = 0,
Expand Down