使用模型生成embedding: 将编码后的输入数据传递给模型,模型将输出对应的embedding。 python with torch.no_grad(): outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state 在这里,last_hidden_states就是生成的embedding。它包含了输
model – Which face detection model to use. “hog” is less accurate but faster on CPUs. “cnn” is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is “hog”. Returns: A list of tuples of found face locations in css (top, right, bottom...
class Model(torch.nn.Module): def __init__(self): super().__init__() self.fc = torch.nn.Linear(768,2) def forward(self,input_ids,attention_mask,token_type_ids): with torch.no_grad(): # 先拿预训练模型做一个计算,抽取数据中的特征, out = pretrained(input_ids=input_ids, attention...
具体用法参考单元测试代码HuggingFaceEmbeddingGenerationTests using System; using System.Collections.Generic; using System.Linq; using System.Net; using System.Net.Http; using System.Threading.Tasks; using Microsoft.SemanticKernel.Connectors.AI.HuggingFace.TextEmbedding; using Xunit; namespace SemanticKernel.C...
Once you have deployed the model you can use the /embed_sparse endpoint 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' The license to use TEI on Habana Gaudi is the one of TEI: ...
embedding = HuggingFaceBgeEmbeddings(model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为文本生成向量表示用于文本检索") # 输入文本并生成向量 text = "这是一个测试文本" vectors = embedding.encode(text) # 打印生成的向量 print(vectors) 在这个实例中...
project = atlas.map_data(embeddings=np.stack(df['embedding'].values), data=df, id_field='id', identifier=identifier, topic_model=topic_options) print(f"Succeeded in creating new version of nomic Atlas: {project.slug}") 鉴于nomic 的工作方式,每次运行atlas.map_data时,它都会在你的帐户下创建...
尽管该模型在基于英语的任务中存储和速度的使用效率高于OpenAI的`text-embedding-ada-002`,但在处理全球范围内的多语言任务(如跨不同社交媒体平台的情感分析)时,这一优势就被浪费了。由于Hugging Face通过这个高性能的gte-small模型以及与Docker兼容性和OpenAPI文档等服务保持了竞争优势,忽视多语言能力在其产品中造成了...
BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(30522, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace...
中文环境下使用 huggingface 模型替换OpenAI的Embedding 接口 但是OpenAI的文本嵌入接口对中文的支持并不好,社区经过实践,对中文支持比较好的模型是Hugging face上的 ganymedenil/text2vec-large-chinese。...具体可以参见:https://huggingface.co/GanymedeNil/text2vec-large-chinese/discussions/3 ,作者采用的训练数据集...