sentence-transformers/distilbert-base-nli-mean-tokens 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. Usage (Sentence-Transformers) Using this model becomes easy when you hav...
roberta-base-nli-max-tokens 是以预训练RoBERTa-base模型在NLI数据集进行微调的SBERT模型,并且该模型使用均值池化策略计算句子表示 distilbert-base-nli-mean-tokens是以预训练DistilBERT-base模型在NLI数据集上进行微调的SBERT模型,并且该模型使用均值池化策略计算句子表示 这样,我们说预...
这个库使用HuggingFace的Transformer,所以我们可以在这里找到sentence-transformers模型:https://huggingface.co/sentence-transformers 我们将使用bert-base-nli-mean-tokens模型,它实现了我们到目前为止讨论的相同逻辑。 (它还使用128个输入token,而不是512个)。 让我们创建一些句子,初始化我们的模型,并对句子进行编码: Wr...
这个库使用HuggingFace的Transformer,所以我们可以在这里找到sentence-transformers模型:https://huggingface.co/sentence-transformers 我们将使用bert-base-nli-mean-tokens模型,它实现了我们到目前为止讨论的相同逻辑。 (它还使用128个输入token,而不是512个)。 让我们创建一些句子,初始化我们的模型,并对句子进行编码: Wr...
bert-base-nli-cls-token:采用预训练的BERT-base模型,用NLI数据集进行微调。使用CLS标记作为句子特征。 bert-base-nli-mean-token:采用预训练的BERT-base模型,并用NLI数据集进行微调。使用平均汇聚策略计算句子特征。 roberta-base-nli-max-tokens:采用预训练的RoBERTa-base模型,并用NLI数据集进行微调。使用最大汇聚...
sent_transformer=SentenceTransformer("bert-base-nli-mean-tokens")questions=["How to improve your conversation skills? ","Who decides the appointment of Governor in India? ","What is the best way to earn money online?","Who is the head of the Government in India?","How do I improve my...
model = SentenceTransformer('bert-base-nli-mean-tokens') # 生成句子嵌入 sentences = [ "今天的天气真好。", "外面阳光明媚!", "他开车去了体育场。", ] embeddings = model.encode(sentences) # 计算相似度 similarities = model.similarity(embeddings, embeddings) ...
Either from scratch of by loading a pre-trained model model = SentenceTransformer('distilbert-base-nli-mean-tokens') #Define your train examples. You need more than just two examples... train_examples = [InputExample(texts=['My first sentence', 'My second sentence'], label=0.8), Input...
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...
bert-base-nli-mean-tokens: BERT-base model with mean-tokens pooling. Performance: STSbenchmark: 77.12 bert-large-nli-mean-tokens: BERT-large with mean-tokens pooling. Performance: STSbenchmark: 79.19 roberta-base-nli-mean-tokens: RoBERTa-base with mean-tokens pooling. Performance: STSbenchmark...