Comments (5)
@MSheshera Do you have anything that could be easily merged?
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Not really. I added some code that converted text in my json files to protobuf examples and then to write predictions back to the json files. But I dont think any of it was as nice as taking a list of tokens and returning labels.
from dilated-cnn-ner.
@MSheshera can you share the code and a sample JSON file
from dilated-cnn-ner.
I dont think I could do that right now because the code is actually part of a larger project. But I can certainly work with @strubell and try to merge that code in here. Sorry I couldn't be of too much immediate help!
from dilated-cnn-ner.
@sbrugman : I beg your pardon but, can we use the commit you've made,for both conll and Ontonotes, such that it takes in text and pumps out their labels?
Is PR (Inference and minor improvements #16) complete?
Thanks
Regards
from dilated-cnn-ner.
Related Issues (20)
- What is the use of projection layer HOT 1
- Questions regarding differences between the implementation and the experiment details in the research paper HOT 5
- Need some clarification on the settings HOT 2
- Training File HOT 1
- Training issue HOT 1
- Is the dilated cnn ner model stable? HOT 2
- Did you try your model on other seq labeling tasks like Chunking or POS? HOT 1
- Nan problem during training on ontonotes data set HOT 7
- Getting some issue with permission beyond my understanding HOT 3
- int() argument must be a string, a bytes-like object or a number, not 'map' HOT 2
- About the paper HOT 6
- details on the accuracy HOT 12
- preprocessing before triggering 'preprocess.sh' for ontonotes HOT 2
- Validate model on real text data HOT 4
- InvalidArgumentError: indices[11,21] = 243838 is not in [0, 243245)
- Inconsistent results when predicting a single sentence versus predicting labels for dev set HOT 4
- Support for Tensorflow 1.13 HOT 4
- Any pytorch version of dilated-cnn-ner around ? HOT 3
- Question for the paper (only)
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