ms-marco-MiniLM-L6-v2
这是一款面向英文场景的文本排序预训练模型，可判断查询与候选文本的匹配度，对召回的文本内容按相关度高低排序。
这个项目值得继续研究吗？
这是一款面向英文场景的文本排序预训练模型，可判断查询与候选文本的匹配度，对召回的文本内容按相关度高低排序。
- 解决什么问题
- 企业在搭建英文知识库问答、内部英文文档检索系统时，初轮召回的候选内容匹配度参差不齐，人工筛选效率低、成本高，无法满足用户实时获取精准结果的需求。
- 适合什么团队
- 适合有英文信息检索、英文知识库问答需求，具备基础AI模型部署能力的业务或技术团队使用。
- 使用前注意
- 该模型仅支持处理英文内容，采用Apache-2.0开源许可可商用，部署需配套对应AI框架运行环境。
本页用于缩短初步筛选时间，不构成技术、采购或法律结论。 正式使用前请在真实业务数据上验证，并以官方说明与许可证为准。
从官方资料看清能力、部署与采用边界
当前先展示可追溯的上游公开说明；系统会在后台补充中文导读，不影响你先核对项目资料。
Cross-Encoder for MS Marco
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query with all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco
Usage with SentenceTransformers
The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2')
scores = model.predict([
("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)
# [ 8.607138 -4.320078]Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec | | ------------- |:-------------| -----| --- | | Version 2 models | | | | cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9000 | cross-encoder/ms-marco-MiniLM-L2-v2 | 71.01 | 34.85 | 4100 | cross-encoder/ms-marco-MiniLM-L4-v2 | 73.04 | 37.70 | 2500 | cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1800 | cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960 | Version 1 models | | | | cross-encoder/ms-marco-TinyBERT-L2 | 67.43 | 30.15 | 9000 | cross-encoder/ms-marco-TinyBERT-L4 | 68.09 | 34.50 | 2900 | cross-encoder/ms-marco-TinyBERT-L6 | 69.57 | 36.13 | 680 | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 | Other models | | | | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 | nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 | nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 | Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 | amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 | sebastian-hofstaetter/distilbert-cat-marginmse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
官方资料与来源
- sentence-transformers
- pytorch
- jax
- onnx
- safetensors
- openvino
- bert
- text-classification
- transformers
- text-ranking
- en
- text-embeddings-inference
核对上游原始说明节选
任务类型：text-ranking
Cross-Encoder for MS Marco
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query with all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco
Usage with SentenceTransformers
The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2')
scores = model.predict([
("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)
# [ 8.607138 -4.320078]Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec | | ------------- |:-------------| -----| --- | | Version 2 models | | | | cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9000 | cross-encoder/ms-marco-MiniLM-L2-v2 | 71.01 | 34.85 | 4100 | cross-encoder/ms-marco-MiniLM-L4-v2 | 73.04 | 37.70 | 2500 | cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1800 | cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960 | Version 1 models | | | | cross-encoder/ms-marco-TinyBERT-L2 | 67.43 | 30.15 | 9000 | cross-encoder/ms-marco-TinyBERT-L4 | 68.09 | 34.50 | 2900 | cross-encoder/ms-marco-TinyBERT-L6 | 69.57 | 36.13 | 680 | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 | Other models | | | | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 | nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 | nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 | Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 | amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 | sebastian-hofstaetter/distilbert-cat-marginmse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
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