from sentence_transformers.training_args import SentenceTransformerTrainingArgumentsargs = SentenceTransformerTrainingArguments(# Required parameter: output_dir="models/mpnet-base-all-nli-triplet",# Optional training parameters: num_train_epochs=1, per_device_train_batch_size=16, per_device_ev...
1、之前使用chatgpt接口生成embeddings的向量维度为1536维,数据库中占用较大,所以找寻低维度的向量生成方法,减少数据占用 2、在huggingface上发现all-mpnet-base-v2及all-MiniLM-L6-v2两个模型不错,前者会生成768维的向量,后者会生成384维的向量 二、介绍: 1、huggingface下的Sentence Transformers是一个Python框架,用...
fromsentence_transformers.training_argsimportSentenceTransformerTrainingArgumentsargs=SentenceTransformerTrainingArguments(# Required parameter: output_dir="models/mpnet-base-all-nli-triplet", # Optional training parameters: num_train_epochs=1, per_device_train_batch_size=16, per_device_eval_batch_size=16,...
all-mpnet-base-v2 This 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. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https...
Sentence Transformers 是一个 Python 库,用于使用和训练各种应用的嵌入模型,例如检索增强生成 (RAG)、语义搜索、语义文本相似度、释义挖掘 (paraphrase mining) 等等。其 3.0 版本的更新是该工程自创建以来最大的一次,引入了一种新的训练方法。在这篇博
Sentence Transformers 是一个 Python 库,用于使用和训练各种应用的嵌入模型,例如检索增强生成 (RAG)、语义搜索、语义文本相似度、释义挖掘 (paraphrase mi...
pairs) and are designed asgeneral purposemodels. Theall-mpnet-base-v2model provides the best quality, whileall-MiniLM-L6-v2is 5 times faster and still offers good quality. ToggleAll modelsto see all evaluated models or visitHuggingFace Model Hubto view all existing sentence-transformers models. ...
all-mpnet-base-v2 multi-qa-mpnet-base-dot-v1 查询与"Probabilistic Analysis of open pit slope stability"最相似的Top10,以Model 1为例其结果如下: [1] (2021) Stability Analysis andOptimal Designof Ultimate Slope of an Open Pit Mine: A Case Study.【经验确定露天矿边坡角的Haines and Terbrugge...
I came acrossthis scriptwhich is second link onthis pageandthis explanationI am usingall-mpnet-base-v2(link) and I am using my custom data I am having hard time understanding use of evaluator = EmbeddingSimilarityEvaluator.from_input_examples( ...
这需要比微调现有的 Sentence Transformer 模型,如 all-mpnet-base-v2,更多的训练数据。 运行此脚本后,tomaarsen/mpnet-base-all-nli-triplet 模型被上传了。使用余弦相似度的三元组准确性,即 cosine_similarity(anchor, positive) > cosine_similarity(anchor, negative) 的百分比为开发集上的 90.04% 和测试集上的...