This repository is BERT-based model service for Sentiment Attitude Extraction, based on AREkit framework.
- Python 2.7.9
- AREkit == 0.20.5
- tqdm
AREkit repository:
# Clone repository in local folder of the currect project.
git clone -b 0.20.5-rc https://github.com/nicolay-r/AREkit ../arekit
# Install dependencies.
pip install -r arekit/requirements.txt
Using run_serialization.sh
in order to prepare data for a particular experiment:
python run_serialization.py
--cv-count 3 --frames-version v2_0
--experiment rsr+ra --labels-count 3 --ra-ver v1_0
--entity-fmt rus-simple --balance-samples True
--bert-input-fmt c_m
For flags meanings please proceed with this section
Proceed with the following notebook.
Common flags:
--experiment
-- is an experiment which could be as follows:rsr
-- supervised learning + evaluation within RuSentRel collection;ra
-- pretraining with RuAttitudes collection;rsr+ra
-- combined training within RuSentRel and RuAttitudes and evalut.
--cv_count
-- data folding mode:1
-- predefined docs separation onto TRAIN/TEST (RuSentRel);k
-- CV-based folding ontok
-folds; (k=3
supported);
--frames_versions
-- RuSentiFrames collection version:v2.0
-- RuSentiFrames-2.0;
--ra_ver
-- RuAttitudes version, if collection is applicable (ra
orrsr+ra
experiments):v1_2
-- RuAttitudes-1.0 paper;v2_0_base
;v2_0_large
;v2_0_base_neut
;v2_0_large_neut
;
--bert-input-fmt
-- supported input formattersc_m
-- single input (TEXT_A);nli_m
-- TEXT_A + context in between of the attitude participants (TEXB_B);qa_m
-- TEXT_A + question.
TODO. To be updated.