Comments (1)
zelin-x
commented on June 2, 2024
3
FastText原论文给的是Subword N-gram保证语序上的语义特征提取，那就许需要同时将这三个东西送入模型，总的模型的输入格式是# x (uni-gram,seq_len,bi-gram,tri-gram)。经过嵌入层后out_word，out_bigram，out_trigram都是32*300的二维张量，在最后一维进行进行拼接保证他们三个的特征同时输入进去。
from chinese-text-classification-pytorch.
Related Issues (20)
- Text_RCNN文本分类效果
- 使用自己的数据集出现问题 HOT 1
- FastText的n_gram_vocab HOT 1
- 一个文本要是有多个标签要怎么用这个模型呢 HOT 3
- 用模型进行预测，该怎么实现呢 HOT 1
- 如果想改成英文数据，都需要修改什么呢
- transformer HOT 2
- 可以写一个单条预测的接口嘛？
- RuntimeError: GET was unable to find an engine to execute this computation
- 是否有文本长度限制
- Bi-LSTM问题
- 是否是没有进行分词？
- 选择题如何定制输入
- 词向量训练问题
- [BUG] TextCNN训练速度很慢
- 请问为何TextCNN的一个iter为什么要十几分钟，而TextRNN就很快，一个epoch不到一分钟？ HOT 1
- 网络结构中Model加载预训练词向量的疑问
- 这个仓是不是没有维护了呀？
- AttributeError: 'Config' object has no attribute 'n_gram_vocab' HOT 1
- 模型预测类 HOT 2
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from chinese-text-classification-pytorch.