Comments (3)
That all depends on what kind of data you're using. A general procedure is:
- load raw data, including input and corresponding label
- tokenize the natural language and get the token list
- build vocab for your dataset
- map the natural language token to index by using the vocab
- go with your network
from disan.
but in your code their is extensive use of trees. i dont understand its purpose
trees = []
with open(pjoin(data_dir, 'STree.txt'), encoding='utf-8') as file_STree,
open(pjoin(data_dir, 'SOStr.txt'), encoding='utf-8') as file_SOStr:
for STree, SOStr in zip(file_STree, file_SOStr):
sent_tree = []
STree = list(map(int, STree.strip().split('|')))
SOStr = SOStr.strip().split('|')
and for standard datasets we don't have tree.txt
from disan.
Similarly not all these files are available for every dataset, can you please give a simple example of text classification where we have train test and valid dataset files only
from disan.
Related Issues (20)
- word embedding HOT 4
- Using tree structure not raw text in SNLI dataset HOT 3
- Perplexity HOT 6
- experiments on MSRP HOT 3
- Word Embedding tune HOT 6
- tensorboard graph did not show anything HOT 1
- report error in line94 snli_main.py
- what to do about the var rep_mask in the disan.py? HOT 10
- Can you take examples for the Fast-Disa.py?
- input to fast-disan.py
- SICK dataset
- Excuse me, where is the code for visual attention weight?
- SST data process HOT 1
- question about config parameter "only_sentence" HOT 3
- Where is the trained model stored?
- How can I generate predictions for my non-SNLI dataset using SNLI-DiSAN model? HOT 1
- It seems that without a pretrained embedding input, the results become worse HOT 2
- The net seems use lstm HOT 2
- About the structure of the code HOT 1
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from disan.