msmarco-bert-co-condensor
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗DISCOVER & COMPARE
AI 모델 탐색 127
msmarco-distilbert-base-dot-prod-v3
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-base-tas-b
sentence-transformersThis is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-base-v2
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-base-v3
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-base-v4
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-cos-v5
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the MS MARCO Passages dataset. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-dot-v5
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500K (query, answer) pairs from the MS MARCO dataset. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-distilroberta-base-v2
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-MiniLM-L12-cos-v5
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the MS MARCO Passages dataset. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-MiniLM-L12-v3
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-MiniLM-L6-cos-v5
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the MS MARCO Passages dataset. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-MiniLM-L6-v3
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-roberta-base-ance-firstp
sentence-transformersThis is a port of the ANCE FirstP Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-roberta-base-v2
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗msmarco-roberta-base-v3
sentence-transformers基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗multi-qa-distilbert-cos-v1
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗multi-qa-distilbert-dot-v1
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗multi-qa-MiniLM-L6-cos-v1
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗multi-qa-MiniLM-L6-dot-v1
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗multi-qa-mpnet-base-cos-v1
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 임베딩
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗multi-qa-mpnet-base-dot-v1
sentence-transformersThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
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