Once you have deployed the model you can use the/embed_sparseendpoint to get the sparse embedding: curl 127.0.0.1:8080/embed_sparse \ -X POST \ -d'{"inputs":"I like you."}'\ -H'Content-Type: application/json' text-embeddings-inferenceis instrumented with distributed tracing using OpenTe...
Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5. TEI implements many features such as: ...
文本嵌入模型的高速推理解决方案 - GitHub - huggingface/text-embeddings-inference:文本嵌入模型的高速推理解决方案
更详细的结构信息,例如RMSNorm、RoPE(Rotary Position Embedding)等,请参考链接。 2.2. 张量并行与模型切分 Attention的权重切分方案 Feed Forward部分的权重切分 张量并行(Tensor Parallel)的系统学习,可以参考这篇文章。笔者简单提醒2点: Attention部分和Feed Forward部分的均涉及2次权重切分和1次All Reduce通信。为使...
Input Embedding负责将前述包含4个元素的Token序列转换为维度为[4, N]的Embedding张量后,数个Transformer Block将Embbeding张量变换得到维度仍为[4, N]的特征张量,将最后一个Token(“快”)对应的特征向量通过最后的Linear升维到词表维度和通过Softmax归一化,得到预测的下一个Token的概率(Tensor对应维度为[1, M],...
这就意味着:即使是同一个样本过两次模型也会得到两个不同的 embedding。而因为同样的样本,那一定是相似的,模型输出的这两个 embedding 距离就应当尽可能的相近;反之,那些不同的输入样本过模型后得到的 embedding 就应当尽可能的被推远。 具体来讲,一个 batch 内每个句子会过 2 次模型,得到 2 * batch 个向量...
To build this, we created a corpus of more than 470,000 h of automatically aligned speech translations (SEAMLESSALIGN) using a new sentence embedding space (Sentence-level Multimodal and Language-Agnostic Representations, or SONAR)8. We then combined a filtered subset of this corpus with human...
在finetune阶段,训练语料由3个数据集的组成:NLI(Natural Language Inference)、MS-MARCO 、NQ(Natural Question)。NLI能够使模型学习句子之间的相似性,MS-MARCO和NQ主要是侧重于检索。 四、训练方法 pretrain阶段 采用InfoNCE contrastive loss,将batch里面的每一对pair作为正样本,而其他样本当作负样本,使模型拉近正样...
With the popularity of distributed representation, pre-trained word embedding models such as word2vec (Mikolov et al., 2013) and glove (Pennington et al., 2014) are also widely used for natural language tasks.Question answering is a long-standing challenge in NLP, and the ...
Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5. TEI implements many features such as: ...