Comments (5)
It's a bit hacky so I wouldn't recommend using it in production. We do the predictions w/o each word and see how much the distribution over predicted emojis change.
from deepmoji.
@bfelbo I see. So if I understand it correctly, is this how it's done?
- Say we have a sentence "I loved the application" and we run deepmoji to get top 5 predictions
- Then, we remove one word at a time and see the predictions
- The words whose removal changed the top 5 predictions are the important ones
If so, how do you give that score in the screenshot above? The importance reflected by the darker and lighter shades of purple.
from deepmoji.
Exactly. The shading is simply an interval over the difference. It was picked manually by trying out a couple of sentences and seeing what seemed to make sense.
Note that you can run all the predictions in a single batch, which will make this pretty fast even for long sentences.
from deepmoji.
I'm closing this for now as it's not related to the functionality of the repo, but to the demo. Hope my response was helpful!
from deepmoji.
It was. Thanks a lot.
from deepmoji.
Related Issues (20)
- Live Demo is down HOT 3
- How did you relate data labels to emojis HOT 1
- Returning "confidence" for each predicted emoji?
- [Errno 2] No such file or directory: 'model/deepmoji_weights.hdf5' HOT 4
- Fine-tuning to predict emotion labels HOT 8
- How to start training deepmoji on a new language corpus? HOT 2
- Building similar model for Hindi(India) language. HOT 1
- Is training dataset available? HOT 1
- How are emojis handled? How are they encoded? HOT 1
- Deploy model in mobile. HOT 2
- Is the training data private? HOT 1
- index error with SCv1 and SCv2-GEN
- Benchmark Dataset Splits
- Deepmoji Live demo is down HOT 2
- cannot connect to remote server HOT 1
- cannot connect to remote server HOT 1
- how to return impact associated to each word per sentence HOT 17
- confusion in finetuning script
- the link https://deepmoji.mit.edu/ is broken HOT 3
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from deepmoji.