readme: ---
  language:
  - en
  ---
  
  # pgdyn-plan
  
  This is a pretrained model for the simplification component of the PG_Dyn system, described in the EACL 2023 paper "Document-Level Planning for Text Simplification". 
  It is the be used in conjunction with [the planning component](https://huggingface.co/liamcripwell/pgdyn-plan) to form the full pipeline. 
  The code [in this repo](https://github.com/liamcripwell/plan_simp) should be used.
  
  ## How to use
  
  Here is how to load this model in PyTorch:
  
  ```python
  from plan_simp.models.classifier import load_planner
  from plan_simp.models.bart import load_simplifier
  
  # contextual simplification planner
  planner, p_tokenizer, p_hparams = load_planner("liamcripwell/pgdyn-plan")
  
  # simplification model
  simplifier, tokenizer, hparams = load_simplifier("liamcripwell/pgdyn-simp")
  ```
  
  To perform end-to-end planning+simplification with dynamic document context, use the commands below. This assumed data is in a `.csv` format and context representations have been generated for each input document.
  
  ```bash
  # using planner
  python plan_simp/scripts/generate.py dynamic 
    --clf_model_ckpt=<planner_model> # e.g. liamcripwell/pgdyn-plan
    --model_ckpt=<simplification_model> # e.g. liamcripwell/pgdyn-simp
    --test_file=<test_sentences>
    --doc_id_col=pair_id # document identifier for each sentence
    --context_doc_id=c_id
    --context_dir=<context_dir>
    --reading_lvl=s_level 
    --out_file=<output_csv>
  
  # manual specification of operations (no planner)
  python plan_simp/scripts/generate.py inference 
    --model_ckpt=<simplification_model> # e.g. liamcripwell/pgdyn-simp
    --test_file=<test_sentences> 
    --op_col=label
    --reading_lvl=s_level 
    --out_file=<output_csv> 
  ```
  