1、预训练模型 - Pretrained Models We provide various pre-trained models. Using these models is easy: 提供了大量预训练模型。使用简单如下: fromsentence_transformersimportSentenceTransformermodel=SentenceTransformer('model_name') All models are hosted on theHuggingFace Model Hub. 所有的模型都托管在抱脸模...
自然问题 NQ数据集模型- Natural Questions (NQ) Dataset Models The following models were trained onGoogle’s Natural Questions dataset, a dataset with 100k real queries from Google search together with the relevant passages from Wikipedia. 下面的模型在谷歌的自然问题数据集上进行训练,该数据集是来自谷歌...
Beyond that, Sentence Transformers supports a lot ofloss functionsand aTrainerthat can help with getting the best performing models for your task. As you can see in therecent Sentence Transformer models, there's a lot of interest in finetuning. There might also already be some finetuned model...
test_evaluator(model)# 8. Save the trained modelmodel.save_pretrained("models/mpnet-base-all-nli-triplet/final")
SentenceTransformerTrainer使用datasets.Dataset或datasets.DatasetDict实例进行训练和评估。你可以从 Hugging Face 数据集中心加载数据,或使用各种格式的本地数据,如 CSV、JSON、Parquet、Arrow 或 SQL。 注意: 许多开箱即用的 Sentence Transformers 的 Hugging Face 数据集已经标记为sentence-transformers,你可以通过浏览http...
('bert-base-uncased')# Apply mean pooling to get one fixed sized sentence vectorpooling_model= models.Pooling(word_embedding_model.get_word_embedding_dimension(),pooling_mode_mean_tokens=True,pooling_mode_cls_token=False,pooling_mode_max_tokens=False)model= SentenceTransformer(modules=[word_...
sentence_transformer training usage dataset_overview.md loss_overview.md pretrained_models.md training_overview.md Makefile conf.py installation.md publications.md quickstart.rst requirements.txt examples sentence_transformers tests .gitignore .pre-commit-config.yaml LICENSE MANIFEST.in Makefile NOTICE.txt...
我们提供了超过100种语言的大量预训练模型。一些模型是通用模型,而其他模型产生特定用例的嵌入。只需传递模型名称即可加载预训练模型:SentenceTransformer('model_name')。 地址:Pretrained Models — Sentence-Transformers documentation from sentence_transformers import SentenceTransformer ...
1、SimCSE通过dropout构建的正例对包含相同长度的信息(原因:Transformer的Position Embedding),会使模型倾向于认为相同或相似长度的句子在语义上更相似; 2、更大的batch size会导致SimCSE性能下降; ESimCSE构建正例对的方法:Word Repetition(单词重复)和 Momentum Contrast(动量对比学习)扩展负样本对。 SimCSE论文: ESim...
而如果是非对称语义搜索问题(query很短,但是需要检索出的answer是一篇比较长的文档),则采用https://www.sbert.net/docs/pretrained-models/msmarco-v3.html中给出的模型。 语义相似度计算: 模型自动下载,并在/root/.cache下创建缓存 model = SentenceTransformer('paraphrase-MiniLM-L12-v2') ...