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2 changes: 1 addition & 1 deletion fastembed/sparse/bm25.py
Original file line number Diff line number Diff line change
Expand Up @@ -237,7 +237,7 @@ def _term_frequency(self, tokens: List[str]) -> Dict[int, float]:

@classmethod
def compute_token_id(cls, token: str) -> int:
return abs(mmh3.hash(token))
return mmh3.hash(token, signed=False)

def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[SparseEmbedding]:
"""To emulate BM25 behaviour, we don't need to use weights in the query, and
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4 changes: 2 additions & 2 deletions fastembed/sparse/bm42.py
Original file line number Diff line number Diff line change
Expand Up @@ -169,7 +169,7 @@ def _rescore_vector(self, vector: Dict[str, float]) -> Dict[int, float]:
new_vector = {}

for token, value in vector.items():
token_id = abs(mmh3.hash(token))
token_id = mmh3.hash(token, signed=False)
# Examples:
# Num 0: Log(1/1 + 1) = 0.6931471805599453
# Num 1: Log(1/2 + 1) = 0.4054651081081644
Expand Down Expand Up @@ -260,7 +260,7 @@ def embed(
def _query_rehash(cls, tokens: Iterable[str]) -> Dict[int, float]:
result = {}
for token in tokens:
token_id = abs(mmh3.hash(token))
token_id = mmh3.hash(token, signed=False)
result[token_id] = 1.0
return result

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