我使用SentenceTransformers库(这里:https://pypi.org/project/sentence-transformers/#pretrained-models)通过预先训练好的模型bert-base-nli-mean-tokens来创建句子的嵌入。我有一个应用程序将部署到不能访问互联网的设备上。如何在本地保存此模型,以便当我调用它时,它会在本地加载模型,而不 ...
公司防火墙似乎阻止我只使用model = AutoModel.from_pretrained("sentence-transformers/bert-base-nli-stsb-mean-tokens") 因此,我需要在本地下载此模型,然后将其读取到Python中。找不到直接的AWS链接,似乎通常是这种形式:但不起作用 https://s3.amazonaws.com/models.huggingface.co/bert/< ...
model = AutoModel.from_pretrained(‘sentence-transformers/bert-base-nli-stsb-mean-tokens’)这里我们使用了预训练的SBERT模型’bert-base-nli-stsb-mean-tokens’,它已经在多个数据集上进行了预训练,可以用于各种自然语言处理任务。加载模型后,就可以使用它来对文本进行编码了。以下是使用SBERT对文本进行编码的示...
首先BERT-STSb-base与BERT-NLI-STSb-base由于数据集NLI带来了明显的性能差异。然后BERT-STSb-base与SBERT-STSb-base直接的差异甚小,乃至large网络时,Bert直接反超SBert。(具体这里为啥会是这个结果呢,作者没说。。) 4.3 争议话题相似度 我们在Argument Facet Similarity (AFS)语料库上评估SBERT。AFS语料库注释了...
# 安装 pip install -U sentence-transformers # 导入包并选择预训练模型 from sentence_transformers import SentenceTransformer as SBert model = SBert('roberta-large-nli-stsb-mean-tokens') # 模型大小1.31G # 对句子进行编码 sentences1 = ['The cat sits outside'] sentences2 = ['The dog plays in...
main distilbert-base-nli-stsb-mean-tokens / tokenizer.json tokenizer.json 455.16 KB 一键复制 编辑 原始数据 按行查看 历史 nreimers 提交于 4年前 . Add new SentenceTransformer model. 1 {"version":"1.0","truncation":null,"padding":null,"added_tokens":[{"id":0,"special":true,"content...
bert-base-nli-stsb-mean-tokens: Performance: STSbenchmark: 85.14 bert-large-nli-stsb-mean-tokens: Performance: STSbenchmark: 85.29 roberta-base-nli-stsb-mean-tokens: Performance: STSbenchmark: 85.40 roberta-large-nli-stsb-mean-tokens: Performance: STSbenchmark: 86.31 distilbert-base-nli-...
bert-base-nli-mean-tokens77.1286.37 bert-large-nli-mean-tokens79.1987.78 bert-base-nli-stsb-mean-tokens85.1486.07 bert-large-nli-stsb-mean-tokens85.2986.66 roberta-base-nli-stsb-mean-tokens85.44- roberta-large-nli-stsb-mean-tokens86.39- ...
bert-base-nli-stsb-mean-tokens85.1486.07 bert-large-nli-stsb-mean-tokens85.2986.66 roberta-base-nli-stsb-mean-tokens85.44- roberta-large-nli-stsb-mean-tokens86.39- distilbert-base-nli-stsb-mean-tokens85.16- Application Examples We present some examples, how the generated sentence embeddings...
model = SentenceTransformer('distilbert-base-nli-mean-tokens')encoded_data = model.encode(data) 为数据集编制索引 我们可以根据我们的用例通过参考指南来选择不同的索引选项。 让我们定义索引并向其添加数据 index = faiss.IndexIDMap(faiss.IndexFlatIP(768))index.add_with_ids(encoded_data, np.array(ran...