Comments (9)
Hi
I have some question about the decoding status? what does it mean , Can you explain it more carefully, THANK YOU!
from ncrfpp.
I can't explain "carefully" to you because you didn't ask the "careful" question.
from ncrfpp.
sorry,may be some net problem , my supplement can't be seen.
IN your demo.decode.config file ,raw_dir is the same as dev set, may be I don't really understand what the role of decode part? if the input is the dev data . what is the output can you give an sample fo raw.out file ?
thank you !
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PS: in the train() function , I can see the "decode test " part , so the process of decode is just the precess of use the raw input to get the Sequence annotation with trained model? am I right?
from ncrfpp.
You can use any file you want to decode in the raw_dir, only if the file has the legal format. The demo is only an example. The output data has the same format as the input file. You can run the code by yourself to get the raw.out.
from ncrfpp.
I don't understand your second question. The "decode dev" and "decode test" in train() is to evaluate the model performance on dev and test data after each training epoch.
from ncrfpp.
抱歉,关于第二个问题,我还是用中文提下问,可能描述的更准确一些,decode的过程是指的是从输入数据到标注好这个序列的过程吗(以序列标注任务为例),就是就像decode test那部分一样,生成了一个标注序列,只是没有evaluate步骤
from ncrfpp.
是的,不过应该也有evaluate步骤
from ncrfpp.
got it ! thanks
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Related Issues (20)
- Conll 使用内存大 HOT 2
- nbest score HOT 1
- Provision for Custom Features? HOT 1
- Bug in forward method when calling _viterbi_decode, mask is not provided
- about requirement HOT 1
- Can I just use the CRF layer? HOT 1
- 大神你好,请教一些关于报错的问题 HOT 1
- About "tcmalloc: large alloc" message and training aborted HOT 3
- Difference between main_parse.py and main.py HOT 1
- Different results on CPU versus GPU HOT 1
- Test Data Format for Chunking
- Experimenting with Transformer models? HOT 1
- Decode config parameter when not using CRF HOT 1
- Reading fasttext word embeddings HOT 2
- About MAX_SENTENCE_LENGTH parameter HOT 2
- Decode error with nbest=0 when not using CRF
- Resume model training from a checkpoint
- 训练一段时间后,f1为-1 HOT 2
- Unknown labels in decoding
- Support trained model serialization to ONNX format HOT 1
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