BERT-based model utils for Sentiment Attitude Extraction task

This repository is BERT-based model service for Sentiment Attitude Extraction, based on AREkit framework.

Utils List

• Data Serialization
• Results Evaluation

Dependencies

• Python 2.7.9
• AREkit == 0.20.5
• tqdm

Installation

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

Usage: Data Serialization

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

Usage: Results Evaluation

Proceed with the following notebook.

Script Arguments Manual

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 onto k-folds; (k=3 supported);
• --frames_versions -- RuSentiFrames collection version:
  • v2.0 -- RuSentiFrames-2.0;
• --ra_ver -- RuAttitudes version, if collection is applicable (ra or rsr+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 formatters
  • c_m -- single input (TEXT_A);
  • nli_m -- TEXT_A + context in between of the attitude participants (TEXB_B);
  • qa_m -- TEXT_A + question.

References

TODO. To be updated.