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---
language:
- en
---
# O->ConBART document simplification system
This is a pretrained version of the document simplification model presented in the Findings of ACL 2023 paper ["Context-Aware Document Simplification"](https://arxiv.org/abs/2305.06274).
It is a system based on a modification to the BART architecture and operates on individual sentences. It is intended to be guided by a document-level simplification planner.
Target reading levels (1-4) should be indicated via a control token prepended to each input sequence ("\<RL_1\>", "\<RL_2\>", "\<RL_3\>", "\<RL_4\>"). If using the terminal interface, this will be handled automatically.
## How to use
It is recommended to use the [plan_simp](https://github.com/liamcripwell/plan_simp/tree/main) library to interface with the model.
Here is how to use this model in PyTorch:
```python
from plan_simp.models.bart import load_simplifier
simplifier, tokenizer, hparams = load_simplifier("liamcripwell/o-conbart")
# dynamic plan-guided generation
from plan_simp.scripts.generate import Launcher
launcher = Launcher()
launcher.dynamic(model_ckpt="liamcripwell/o-conbart", clf_model_ckpt="liamcripwell/pgdyn-plan", **params)
```
Generation and evaluation can also be run from the terminal.
```bash
python plan_simp/scripts/generate.py dynamic
--clf_model_ckpt=liamcripwell/pgdyn-plan
--model_ckpt=liamcripwell/o-conbart
--test_file=<test_data>
--doc_id_col=pair_id
--context_dir=<context_dir>
--reading_lvl=s_level
--context_doc_id=c_id
--out_file=<output_csv>
python plan_simp/scripts/eval_simp.py
--input_data=newselaauto_docs_test.csv
--output_data=test_out_oconbart.csv
--x_col=complex_str
--r_col=simple_str
--y_col=pred
--doc_id_col=pair_id
--prepro=True
--sent_level=True
```
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