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Hi guys, thanks for your great repo.
I want to ask some question
1. What is the similarity distribution of model when I set temperature = 0.02? Previously, I saw you say that when temperature=0.01, …
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It would be worth to provide an example of using ColBERT passage retrieval from a user query as browser execution.
A good example has been provide [here](https://colbert.aiserv.cloud/) for query-pass…
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Very good paper.
I hope to have a detailed introduction to the "basic training form of dense retrieval" mentioned in the Distant supervision section on page 5. Does it train the query and answer of M…
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Need to add passage-level retrieval. Aim for the coolest thing possible, which, I think, is:
- Allow users to specify how each "retrieval item" is processed. Default implementation is 1 result per can…
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Hello,
I ran the code provided for LongBench using the Llama-3-8B-Instruct model but couldn't reproduce the results reported in Table 8 of your paper. Specifically, the full precision baseline mode…
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```
datasets = ['hotpotqa', '2wikimqa', 'musique', 'narrativeqa', 'qasper', 'multifieldqa_en', 'gov_report', 'qmsum', 'trec', 'samsum', 'triviaqa', 'passage_count', 'passage_retrieval_en', 'multi_new…
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cd retrieval_lm
python passage_retrieval.py \
--model_name_or_path facebook/contriever-msmarco --passages psgs_w100.tsv \
--passages_embeddings "wikipedia_embeddings/*" \
--data YOUR_I…
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bge-multilingual-gemma2是一个基于LLM的模型,从调用方法上来说,需要输入提示词,这个和一般的embedding模型不一样,请问这个能带来什么优势吗,是否可以拿一个场景举例说明一下呢?
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## 一言でいうと
オープンドメインのQAで必要な回答が含まれていそうな文書(Passage)の抽出について、既存のTF-IDFやBM25よりベクトル特徴の内積を使用した抽出の方が1~2割精度が改善するという研究結果。Q、Aは別個のBERTでEncodeされ、実行時は FAISSで近傍ベクトルを抽出する。
### 論文リンク
https://arxiv.org/abs/2004.…
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A100测试
code:
```
import time
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('/home/admin/bge-m3', use_fp16=True)
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
…