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**In the paper, section 3.1 BERT, it is said that -**
`we extract a fixed sized vector via max pooling of the second to last layer.` then `A sentence of N words will hence
esult in an N ∗ H embeddi…
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- Bi-Encoder는 기존 semantic search 방식으로, query와 target 문장을 각각 sentence vector화 시키고, cosine-similarity 등으로 탐색하는 방식
- Cross-Encoder는 query와 target 문장을 입력으로 넣고 유사도 자체를 모델 output으로 얻는 방식 (sentence vector …
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### Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a sim…
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**Is your feature request related to a problem? Please describe.**
Nowaday search can be based by BM25 and semantic search. Using cosine similarity with a provided vector at index time we could ponde…
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Hello :)
First thank you for your amazing work!
When using bge-m3 within langchain what is the default representation of the encoding? Dense or a mix of differents (sparse...)?
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Hi! I want to start out by emphasizing how big a fan I am of the SentenceTransformers module and a large part of my research is depending on this module--great work you’re doing, highly appreciated!
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Hello, as a beginner in NLP analysis I am trying to figure out how to use the sentence embeddings of Sbert for a sentence classification task. From the description [here](https://www.sbert.net/docs/tr…
zsun9 updated
2 years ago
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Hi,the team of ogb. Thanks for your efforts on collecting and processing these valuable datasets! You said you used a RoBERTa sentence encoder to generate a 768-dimensional vector for each paper node …
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我的cppjieba.py
```
#encoding=utf-8
from ctypes import *
import os
cur_dir = os.path.dirname( os.path.abspath(__file__)) or os.getcwd()
lib = cdll.LoadLibrary(cur_dir+'/libJieba.so')
lib.Jieba_…
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### Contact Details [Optional]
makkarss929@gmail.com
### System Information
system : mac m1 pro 14 inch
db : postgres
### What happened?
While creating vector index
```
# Import the …