代码:embeddings-benchmark/mteb :大规模文本嵌入评估 中文文本嵌入评估:CMTEB 向量的检索 向量搜索库 Approximate Nearest Neighbor(ANN)是一种用于在大规模数据集中寻找最近邻居的算法。其目标是在尽可能短的时间内找到与给定查询点最近的数据点,但不一定是确切的最近邻。为了达到这个目标,ANN使用了一些启发式方法,例...
针对text embedding,对于自动编码任务有两个要求,其一是重建任务需要足够难,从而迫使模型去生成高质量的句向量,其二是能够充分利用训练数据。 BGE的预训练采用了RetroMAE的方案,包括一个以Bert为基底的Encoder跟一个只有一层的Decoder,训练时,Encoder端以30%的比例对原文本进行mask,最终得到最后一层[CLS]位置的向量表征...
Firstly, the text is encoded by T5 tokenizers and input to the embedding layer to get the initial text embedding. Next, it goes through the T5 encoder (n-layer T5 Block) to obtain Text Embeddings. Fig. 1 Overall architecture of Swinv2-Imagen. The text is passed through both a frozen ...
中文长文本分类、短句子分类、多标签分类、两句子相似度(Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short),字词句向量嵌入层(embeddings)和网络层(graph)构建基类,FastText,TextCNN,CharCNN,TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert...
()fordocincorpus]input_texts=['{}'.format(t)fortininput_texts]returnself._do_encode(input_texts)@torch.no_grad()def_do_encode(self,input_texts:List[str])->np.ndarray:returnself.encoder.encode(sentences=input_texts,batch_size=512,normalize_embeddings=True,convert_to_numpy=True)defget_args(...
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(input_texts)@torch.no_grad()def_do_encode(self,input_texts:List[str])->np.ndarray:returnself.encoder.encode(sentences=input_texts,batch_size=512,normalize_embeddings=True,convert_to_numpy=True)defget_args():parser=argparse.ArgumentParser()parser.add_argument('--model_name_or_path',default=...
First text input is encoded with text encoder, and then implicit and explicit information are added to hidden embeddings from text encoder, which is then used to predict the mel-spectogram with a spectrum decoder. Lastly, the vocoder is used to convert mel-spectogram i...
Self-Supervised pre-training can be used via two methods or routines which we refer as: encoder-decoder method and constrastive-denoising method. Please, see the documentation and the examples for details on this functionality, and all other options in the library....
Use BERT pre-trained word embeddings as input to the Bi-LSTM model. Text-GCN: A text classification method that uses graphs to model text. The words and documents in the text are regarded as nodes, where the edges of the document and the words are based on the appearance information of ...