我在序列分类-二进制问题中使用了长运算器。
我已经下载了所需的文件
# load model and tokenizer and define length of the text sequence
model = LongformerForSequenceClassification.from_pretrained('allenai/longformer-base-4096',
gradient_checkpointing=False,
我想应用这个方法来实现Bi-LSTM。这里讨论了该方法:Bi-LSTM Attention model in Keras 我得到以下错误:'module' object is not callable 它不能在以下行中应用乘法:sent_representation = merge([lstm, attention], mode='mul') from keras.layers import merge
import tensorflow as tf
from tensorflow.keras.layers import Concatenate, Dense,
如何在文本文件中打印Mutt用户代理收件箱列表?
这是我的问题:
我需要打印收件箱文件列表,这些文件具有以下设置:
set index_format="%4C %Z %{%d/%m/%y %H:%M} %s"
这样,我需要在文本文件中打印它们,并包含以下示例内容:
10 N F 08/07/19 08:53 Attention: alarm(14286247:motion detection) 11 N F 08/07/19 08:53 Attention: alarm(14033396:motion detection) 12 N F 08/07/19 08:53 A
我正在使用Swin变压器来解决多值多标签分类的分层问题。我想形象化自我关注的地图在我的输入图像试图从模型中提取他们,不幸的是,我没有成功地完成这项任务。你能告诉我怎么做吗?我向您介绍了代码中我试图完成此任务的部分。
attention_maps = []
for module in model.modules():
#print(module)
if hasattr(module,'attention_patches'): #controlla se la variabile ha l' attributo
print(module.a
在运行RNN教程时,在读取数据行语句后会出现以下错误:
reading data line 22500000
W tensorflow/core/common_runtime/executor.cc:1052] 0x3ef81ae60 Compute status: Not found: ./checkpoints_directory/translate.ckpt-200.tempstate15092134273276121938
[[Node: save/save = SaveSlices[T=[DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT
我使用的是neurosky mindwave工具包,所以我下载了Neuropy库来获取工具包的读数,我尝试了一个示例代码: from NeuroPy.NeuroPy import NeuroPy
from time import sleep
neuropy = NeuroPy()
def attention_callback(attention_value):
"""this function will be called everytime NeuroPy has a new value for attention"""
希望做一些类似的事情 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = o
我正在运行一个自定义代码来在tensorflow上训练我自己的Seq2Seq模型。我使用的是多核神经网络细胞和embedding_attention_seq2seq。在恢复模型时,我得到了以下错误:
2017-07-14 13:49:13.693612: W tensorflow/core/framework/op_kernel.cc:1158] Not found: Key embedding_attention_seq2seq/rnn/embedding_wrapper/multi_rnn_cell/cell_1/basic_lstm_cell/kernel not found in ch
我正在研究一种语言翻译模式。
1. I want to visualize data as mentioned in [http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/](http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/) using bleu score.
2.
for a in xrange(num_heads):
with variable_scope.variabl
在的变压器单元中,有一些模块称为查询、键和值,或者简单地称为Q、K、V。
基于伯特和 (特别是在中),我对单个注意头的注意模块(使用Q、K、V)向前通过的伪码理解如下:
q_param = a matrix of learned parameters
k_param = a matrix of learned parameters
v_param = a matrix of learned parameters
d = one of the matrix dimensions (scalar value)
def attention(to_tensor, from_tensor, atten
你好,我有一个很大程度上不平衡的数据集,我正在尝试在使用bert之前合并SMOTE。然而,当我将我的数据分成训练,验证和测试时,我有点困惑,不知道该如何做?因为im使用文本数据,所以它是在标记化之后发生的吗?
代码片段:
def tokenize(df):
input_ids = []
attention_masks = []
for i, text in enumerate(df["tidy_tweet"]):
tokens = tokenizer.encode_plus(text, max_length=SEQ_LEN,
我试图用以下代码将火把-闪电模型(ckpt)更改为onnx:.
test_comment = 'I am still waiting on my card?'
start= time.time()
encoding = tokenizer.encode_plus(
test_comment,
add_special_tokens=True,
max_length=512,
return_token_type_ids=False,
padding="max_length",
return_attention_mask=True,
r
全,
我在seq2seq任务中使用了类似于bucketing的技术:
# For different length in encoder and decoder
model_map = {}
for i in encoder_shape:
for j in decoder_shape:
with variable_scope.variable_scope(variable_scope.get_variable_scope(),
reuse=True if tt > 0 else None):
这是我代码的一部分。
from transformers import BertTokenizer,BertForSequenceClassification,AdamW
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',do_lower_case = True,truncation=True)
input_ids = []
attention_mask = []
for i in text:
encoded_data = tokenizer.encode_plus(
i,
ad