officialpatterson / medical-bert Goto Github PK
View Code? Open in Web Editor NEWA repo to contain all the code relevant to the Clinical NLP on readmission prediction
A repo to contain all the code relevant to the Clinical NLP on readmission prediction
It might be that this code has old dependencies.
Start a fresh venv on a development server and run the code to see whats missing/needed.
Currently, all output data is stored locally on the server on which the code is run. The problem with this is that as the number of experiments increases, we end up using more of the disk space. This is both expensive and difficult to share.
Therefore, we need to be able to choose a GCP bucket as an output directory.
Additionally, given that the software is sued to evaluate by others, we must maintain the old method of allowing a localfilesystem store.
Currently, the official version of BERT and the one that we work with the now which performs a tanh activation on the CLS token hidden state, where the hidden state is a vector of 768. The output of this layer is what the classification head uses as its features.
The next step therefore is to replace this BertPooler with a layer that performs mean pooling of the hidden states of all tokens apart from the CLS and SEP tokens.
For the sake of comparison, tanH activation can then be applied to this layer before giving to the FC head.
Currently, we save the experiment config as a JSON file so that we know what has been run. To replicate an experiment found in a JSON file we need to either add each one to the application argument list or to modify the default JSON file.
This issue intends to add a new flag so that when a JSON file is specified we read in config from that instead.
The L3C code released start of December performs better than the most recent release. Attempt to replicate the results. Report back the results, the difference in results, and the difference in code.
Currently, the official version of BERT and the one that we work with the now which performs a tanh activation on the CLS token hidden state, where the hidden state is a vector of 768. The output of this layer is what the classification head uses as its features.
The next step therefore is to replace this BertPooler with a layer that performs LSTM of the hidden states of all tokens apart from the CLS and SEP tokens.
For the sake of comparison, tanH activation can then be applied to this layer before giving to the FC head.
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