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@ -4,7 +4,7 @@ tags:
- sentence-transformers - sentence-transformers
- feature-extraction - feature-extraction
- sentence-similarity - sentence-similarity
license: mit
--- ---
For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding
@ -84,15 +84,16 @@ pip install -U FlagEmbedding
from FlagEmbedding import BGEM3FlagModel from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3', model = BGEM3FlagModel('BAAI/bge-m3',
batch_size=12, #
max_length=8192, # If you don't need such a long length, you can set a smaller value to speed up the encoding process.
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
sentences_1 = ["What is BGE M3?", "Defination of BM25"] sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"] "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
embeddings_1 = model.encode(sentences_1)['dense_vecs'] embeddings_1 = model.encode(sentences_1,
batch_size=12,
max_length=8192, # If you don't need such a long length, you can set a smaller value to speed up the encoding process.
)['dense_vecs']
embeddings_2 = model.encode(sentences_2)['dense_vecs'] embeddings_2 = model.encode(sentences_2)['dense_vecs']
similarity = embeddings_1 @ embeddings_2.T similarity = embeddings_1 @ embeddings_2.T
print(similarity) print(similarity)
@ -162,13 +163,17 @@ sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"] "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
sentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2] sentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2]
print(model.compute_score(sentence_pairs))
print(model.compute_score(sentence_pairs,
max_passage_length=128, # a smaller max length leads to a lower latency
weights_for_different_modes=[0.4, 0.2, 0.4])) # weights_for_different_modes(w) is used to do weighted sum: w[0]*dense_score + w[1]*sparse_score + w[2]*colbert_score
# { # {
# 'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142], # 'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142],
# 'sparse': [0.05865478515625, 0.0026397705078125, 0.0, 0.0540771484375], # 'sparse': [0.195556640625, 0.00879669189453125, 0.0, 0.1802978515625],
# 'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625], # 'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625],
# 'sparse+dense': [0.5266395211219788, 0.2692706882953644, 0.2691181004047394, 0.563307523727417], # 'sparse+dense': [0.482503205537796, 0.23454029858112335, 0.2332356721162796, 0.5122477412223816],
# 'colbert+sparse+dense': [0.6366440653800964, 0.3531297743320465, 0.3487969636917114, 0.6618075370788574] # 'colbert+sparse+dense': [0.6013619303703308, 0.3255828022956848, 0.32089319825172424, 0.6232916116714478]
# } # }
``` ```
@ -221,7 +226,3 @@ If you find this repository useful, please consider giving a star :star: and cit
``` ```
``` ```