Comments (2)
Hi, thank you for your interest in our work. Yes, meta_data_vocab you have is correct. It contains the actual sentences themselves. So that when we print the final predictions of the system, we print the corresponding sentence along with it.
To achieve what you want, one simple solution is to pass the meta_data_vocab to the forward function instead at the time of initialization. Does that make sense?
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Much thanks for your input! I was able to do what I wanted by declaring the model without meta_data_vocab
then setting model._meta_data_vocab
at the later time, before each inference. A natural follow up question is--would I be able to infer each input text without having to declare a new trainer
object every time? The software allows me to do so, but seems to use an indexing of some sort from previous dataloader objects.
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Related Issues (20)
- Possible error when computing final loss HOT 1
- Question about IL embeddings in your paper HOT 2
- Unable to train constraint model HOT 4
- When task = ='conj ', there is a problem with the training data format HOT 1
- Using a different dataset to train, validate, and test HOT 1
- crash with curr_line_num referenced before assignment HOT 1
- Is there a way to get dependency parse outputs in addition to OpenIE ones? HOT 2
- error for downloading model HOT 1
- [Bug] `greenlet.greenlet size changed` error when calling run.py HOT 1
- How can I use data_processing.py to create my own datasets?
- Running as Server
- Missing allennlp.data
- File Deserialization Error during Splitpredict Command Execution with Pretrained Model.
- error in calculating loss?
- Is the manual evaluation available?
- where is the carb/data/test_gold_allennlp_format.txt mentioned in carb/evaluat_all.sh ? HOT 1
- Can you provide the original PTB dataset used in your work? HOT 3
- Conjunction analysis code HOT 1
- Training Warmup model fails HOT 5
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