bertVersusAll

For Using the Traditional Embed-Encode-Attend-Predict Model

Install: pip install -U bert-serving-server bert-serving-client
- The server MUST be running on Python >= 3.5 with Tensorflow >= 1.10
- Download a bert model: SMALL = https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip LARGE = https://storage.googleapis.com/bert_models/2018_10_18/cased_L-24_H-1024_A-16.zip
- Unzip to a directory to point to later (Suggested inside venv folder)
Install other needed libraries and download data
- pip install keras_metrics keras pandas numpy sklearn
Serve Model:
- bert-serving-start -model_dir ~/bert/models/cased_L-12_H-768_A-12/ -num_worker=2 -max_seq_len=250 num_workers can be used to increase num_workers (Model is loaded n-times) max_seq_len specifies the largest entry size to the bert model (suggest 250 for concept work)

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
att_emb_enc		att_emb_enc
data		data
minimal_bert		minimal_bert
.gitignore		.gitignore
1. bert_testing.ipynb		1. bert_testing.ipynb
2.1 attend_encode_attent_input_part1_params.ipynb		2.1 attend_encode_attent_input_part1_params.ipynb
2.2 attend_encode_attent_input_part1_params_solo.ipynb		2.2 attend_encode_attent_input_part1_params_solo.ipynb
3. results_vizualization.ipynb		3. results_vizualization.ipynb
README.md		README.md