构建dict_rcv1/doc_label.dict 时采用的是json文件而非taxonomy文件,about tencent/neuralnlp-neuralclassifier

Comments (9)

MagiaSN commented on May 27, 2024

你好，如果标签在训练数据中不存在，那么加入doc_label.dict中似乎也没有意义，因为训练正常的模型不会把任何样本分类到这个标签下。

from neuralnlp-neuralclassifier.

alisea47 commented on May 27, 2024

那请教一下另一个问题，我把数据换成自己的数据集之后，也修改了conf中对应的参数，会出现如下的错误：
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [64,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [65,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [66,0,0] Assertion srcIndex < srcSelectDimSize failed.
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/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [90,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [91,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [92,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [93,0,0] Assertion srcIndex < srcSelectDimSize failed.
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/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [105,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [106,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [107,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [108,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [109,0,0] Assertion srcIndex < srcSelectDimSize failed.
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/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [112,0,0] Assertion srcIndex < srcSelectDimSize failed.
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/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [114,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [115,0,0] Assertion srcIndex < srcSelectDimSize failed.
/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [183,0,0], thread: [116,0,0] Assertion srcIndex < srcSelectDimSize failed.
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/pytorch/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [139,0,0], thread: [94,0,0] Assertion srcIndex < srcSelectDimSize failed.
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Traceback (most recent call last):
File "train.py", line 265, in
train(config)
File "train.py", line 229, in train
trainer.train(train_data_loader, model, optimizer, "Train", epoch)
File "train.py", line 103, in train
ModeType.TRAIN)
File "train.py", line 122, in run
logits = model(batch)
File "/anaconda/envs/NeuralNLP/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/NeuralNLP-NeuralClassifier-master/model/classification/hmcn.py", line 102, in forward
length = batch[cDataset.DOC_TOKEN_LEN].to(self.config.device)
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

from neuralnlp-neuralclassifier.

alisea47 commented on May 27, 2024

如果将conf中的"device":换成"cpu"，报错信息如下：

Traceback (most recent call last):
  File "train.py", line 265, in <module>
    train(config)
  File "train.py", line 229, in train
    trainer.train(train_data_loader, model, optimizer, "Train", epoch)
  File "train.py", line 103, in train
    ModeType.TRAIN)
  File "train.py", line 122, in run
    logits = model(batch)
  File "/anaconda/envs/NeuralNLP/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "NeuralNLP-NeuralClassifier-master/model/classification/hmcn.py", line 101, in forward
    batch[cDataset.DOC_TOKEN].to(self.config.device))
  File "/anaconda/envs/NeuralNLP/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "NeuralNLP-NeuralClassifier-master/model/embedding.py", line 96, in forward
    embedding = self.embedding(vocab_ids)
  File "/anaconda/envs/NeuralNLP/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File “/anaconda/envs/NeuralNLP/lib/python3.6/site-packages/torch/nn/modules/sparse.py", line 160, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File  “/anaconda/envs/NeuralNLP/lib/python3.6/site-packages/torch/nn/functional.py", line 2044, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self

from neuralnlp-neuralclassifier.

alisea47 commented on May 27, 2024

补充一下，在出现上述错误之前，我们构建了和样本数据相同格式的数据集，但是由于我们的标签层次为2层或4层，没有1层或3层，举个例子，我们的数据只有如下的层次标签：

A1--A11
A1--A12
B1--B11--B111--B1111
B1--B11--B111--B1112

而没有如下层次标签：

A1
B1--B11--B112

运行代码后出现error1：

Traceback (most recent call last):
  File "train.py", line 265, in <module>
    train(config)
  File "train.py", line 215, in train
    model = get_classification_model(model_name, empty_dataset, conf)
  File "train.py", line 83, in get_classification_model
    model = globals()[model_name](dataset, conf)
  File "NeuralNLP-NeuralClassifier-master/model/classification/hmcn.py", line 59, in __init__
    test2 = self.hierarchical_class[i-1]
IndexError: list index out of range

按照代码中的层次标签构建规则，我们修改了conf文件中的

"HMCN": {
        "hierarchical_depth": [0, 384, 384],    //只有两种层次结构
        "global2local": [0, 98, 1343]     //98为A1--A11这样的层次标签数量，1343为B1--B11--B111--B1112这样的层次标签数量
}

之后出现我前面comment中提到的错误。

此外，如果我们仅保留您原始代码中给出的样本数据（data/rcv1_train.hierar.json、rcv1_dev.hierar.json、rcv1_test.hierar.json）的前5行，同样报错error1，所以我觉得这可能是构建层次标签的代码有一些问题。

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MagiaSN commented on May 27, 2024

srcIndex < srcSelectDimSize这个错误是因为token_id超出了embedding词表大小，与标签无关
error1这个错误只和config中HMCN的配置有关，和label_dict无关，鉴于错误信息和源码对不上，可能是有修改，暂时没法进一步排查
基于这个应用场景，如果直接运行训练有困难，这里有两点建议：
1、将四级标签映射到两级进行训练，使用时再将模型预测结果映射回四级；
2、将缺失的第一级、第三级标签也加入训练数据中，例如一条样本标签为"A1--A11"，那么把"A1"也加入变为["A1", "A1--A11"]

from neuralnlp-neuralclassifier.

alisea47 commented on May 27, 2024

您好，按照您昨晚的建议，我又重新梳理了我的错误，确实我上面描述的是两个错误。

error1这个错误只和config中HMCN的配置有关，我修改配置信息后就能解决。
srcIndex < srcSelectDimSize这个错误是因为token_id超出了embedding词表大小，我可否理解为是我的训练数据中出现了太多的不同单词导致的呢？如果是的话，需要修改哪些参数？

非常感谢您的解答！

from neuralnlp-neuralclassifier.

MagiaSN commented on May 27, 2024

您好，按照您昨晚的建议，我又重新梳理了我的错误，确实我上面描述的是两个错误。

error1这个错误只和config中HMCN的配置有关，我修改配置信息后就能解决。

srcIndex < srcSelectDimSize这个错误是因为token_id超出了embedding词表大小，我可否理解为是我的训练数据中出现了太多的不同单词导致的呢？如果是的话，需要修改哪些参数？

非常感谢您的解答！

词表是从数据集生成或从预训练embedding中读取的，具体由config["data"]下面的配置项控制：

generate_dict_using_json_files：由训练集中的token生成词表；
generate_dict_using_all_json_files：由训练集+验证集+测试集中的token生成词表；
generate_dict_using_pretrained_embedding：由预训练embedding读取词表。

生成词表时，是按词频排序取top保留在词表中，词表大小由config["feature"]["max_token_dict_size"]控制，默认会把不在词表中的token都映射到UNK，所以一般不会出现上面的问题，可以再具体调试下。

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zixun-lan commented on May 27, 2024

您好，首先非常感谢开源代码，但是我在运行HMCN模型时，将数据替换为自己的数据集后，发现了一个问题。

从逻辑上说，dict_rcv1/doc_label.dict 文件中应存放所有可能的层次关系，也就是说，该文件应由记录层次关系的rcv1.taxonomy文件生成。例如rcv1.taxonomy文件记录的层次信息如下：
ROOT A B
A A1 A2
A1 A11 A12
B B1
则生成的doc_label.dict 文件信息为：
A value1
B value2
A--A1 value3
A--A2 value4
B--B1 value5
A--A1--A11 value6
A--A1--A12 value7
但是现在的代码是依赖data/rcv1_train.hierar.json、rcv1_dev.hierar.json、rcv1_test.hierar.json文件生成的doc_label.dict 文件，如果在这些文件的"doc_label"中从未出现A--A1--A11这个层次关系，则在doc_label.dict 文件中也不会出现，模型无法预测A--A1--A11。

我们是否应该通过记录层次关系的rcv1.taxonomy文件生成所有可能的层次关系？

您好我有一些关于多层一类关系的问题。
1、rcv1.taxonomy文件内容如何根据现实中的层级关系构建，示例rcv1.taxonomy中：各个字符代表什么意思？

ROOT A B
A A1 A2
A1 A11 A12
B B1

2、构造样本的时候，doc_label如何构造，doc_label中的内容与 rcv1.taxonomy中的内容如何对应？
{
"doc_label": ["Computer--MachineLearning--DeepLearning", "Neuro--ComputationalNeuro"],
"doc_token": ["I", "love", "deep", "learning"],
"doc_keyword": ["deep learning"],
"doc_topic": ["AI", "Machine learning"]
}

from neuralnlp-neuralclassifier.

jamestang0219 commented on May 27, 2024

您好，按照您昨晚的建议，我又重新梳理了我的错误，确实我上面描述的是两个错误。

error1这个错误只和config中HMCN的配置有关，我修改配置信息后就能解决。

srcIndex < srcSelectDimSize这个错误是因为token_id超出了embedding词表大小，我可否理解为是我的训练数据中出现了太多的不同单词导致的呢？如果是的话，需要修改哪些参数？

非常感谢您的解答！

无进一步问题，先close掉了

from neuralnlp-neuralclassifier.

构建dict_rcv1/doc_label.dict 时采用的是json文件而非taxonomy文件 about neuralnlp-neuralclassifier HOT 9 CLOSED

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