---
language:
- en
---

# pgdyn-plan

This is a pretrained model for the planning 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 simplification component](https://huggingface.co/liamcripwell/pgdyn-simp) 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> 
```