piccolo-base-zh

86
35
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by
sensenova
Embedding Model
OTHER
New
86 downloads
Early-stage
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Quick Summary

piccolo是一个通用embedding模型(中文), 由来自商汤科技的通用模型组完成训练。piccolo借鉴了E5以及GTE的训练流程，采用了两阶段的训练方式。 在第一阶段中，我们搜集和爬取了4亿的中文文本对(可视为弱监督文本对数据)，并采用二元组的softmax对比学习损失来优化模型。 在第二阶段中，我们搜集整理...

Code Examples

Usagepython
# for s2s dataset, you can use piccolo as below
# 对于短对短数据集，下面是通用的使用方式
from sentence_transformers import SentenceTransformer
sentences = ["数据1", "数据2"]
model = SentenceTransformer('sensenova/piccolo-base-zh')
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

# for s2p dataset, we recommend to add instruction for passage retrieval
# 对于短对长数据集，我们推荐添加instruction，来帮助模型更好地进行检索。
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["doc_1", "doc_2"]

model = SentenceTransformer('sensenova/piccolo-base-zh')
q_embeddings = model.encode(["查询：" + q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(["结果：" + p for p in passages], normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
Usagepython
# for s2s dataset, you can use piccolo as below
# 对于短对短数据集，下面是通用的使用方式
from sentence_transformers import SentenceTransformer
sentences = ["数据1", "数据2"]
model = SentenceTransformer('sensenova/piccolo-base-zh')
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

# for s2p dataset, we recommend to add instruction for passage retrieval
# 对于短对长数据集，我们推荐添加instruction，来帮助模型更好地进行检索。
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["doc_1", "doc_2"]

model = SentenceTransformer('sensenova/piccolo-base-zh')
q_embeddings = model.encode(["查询：" + q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(["结果：" + p for p in passages], normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
Usagepython
# for s2s dataset, you can use piccolo as below
# 对于短对短数据集，下面是通用的使用方式
from sentence_transformers import SentenceTransformer
sentences = ["数据1", "数据2"]
model = SentenceTransformer('sensenova/piccolo-base-zh')
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

# for s2p dataset, we recommend to add instruction for passage retrieval
# 对于短对长数据集，我们推荐添加instruction，来帮助模型更好地进行检索。
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["doc_1", "doc_2"]

model = SentenceTransformer('sensenova/piccolo-base-zh')
q_embeddings = model.encode(["查询：" + q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(["结果：" + p for p in passages], normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T

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