similarity = similarity_fn(query_embedding, emb) File "/usr/local/lib/python3.8/site-packages/llama_index/embeddings/base.py", line 50, in similarity product = np.dot(embedding1, embedding2) File "<array_function internals>", line 180, in dot ...
if config.encoder_layer == 'transformer': self.attn_fn = TransformerAttention(config) elif config.encoder_layer == 'performer': self.attn_fn = PerformerAttention(config) else: raise NotImplementedError def forward(self, x, mask, pos_emb): h = self.n_heads q = self.to_q(x) k = self...
# 先定义一个Embedding层:emb=Embedding(num_embeddings=3,embedding_dim=5) In [16]: # 转换第一个元素emb(torch.tensor([0],dtype=torch.int64)) Out[16]: tensor([[ 0.6589, 0.4041, 1.1573, -2.3446, -0.1704]], grad_fn=<EmbeddingBackward0>) In [18]: # 转换第二个元素emb(torch.tensor([1...
Cell and tissue shape and structure may predict function; thus, morphological examinations can identify alterations and explain dysfunctions and diseases. This makes histological observation a valuable device for a more detailed characterization of teratogenicity [6]. Due to technological advances in ...
len(emb), type(emb) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. from openai.embeddings_utils import get_embedding, cosine_similarity # 注意它默认的模型是text-similarity-davinci-001,我们也可以换成text-embedding-ada-002 ...
I have made the deprecation into a decorator (vllm.utils.deprecate_args) where the original args are directly passed through to this function. This should eliminate any chances of a breaking change. DarkLight1337 added 2 commits October 17, 2024 03:16 Move tests 323ff33 Make deprecation ...
(state, self.p, loss), _ = theano.scan(fn=self.scan_func, sequences=[cap[0:-1, :], cap[1:, :]], outputs_info=[init_state,None,None], non_sequences=[scene])# loss functionloss = T.mean(loss) self.costs = [loss]# layers and parametersself.layers = [self.embedding, self....
而是input_pipe和loss function,所以在实现的时候也希望尽可能把dataset, model_fn, 和train的部分分割开来。以下只给出model_fn的核心部分def avg_pooling_embedding(embedding, features, params): """ :param features: (batch, 2*window_size) :param embedding: (vocab_size, emb_size) :return: input_...
loss_function = nn.CosineEmbeddingLoss() loss = loss_function(pred_n, target_n, Variable(torch.Tensor(pred_n.size(0)).cuda().fill_(1.0)))returnloss 开发者ID:JiaxiongQ,项目名称:DeepLiDAR,代码行数:15,代码来源:trainN.py 示例3: nomal_loss ...
skip-gram是用中间词来预测周围单词,skip-Thought是用中间句子来预测前一个句子和后一个句子,模型思路就是这么简单粗暴,具体实现就涉及到句子的信息要如何提取,以及loss function的选择。作者选择了encoder-decoder来提取句子信息,用翻译模型常用的log-perplrexity作为loss。 这里想提一句不同模型,在不同的样本上,训练...