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--- |
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language: |
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- en |
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--- |
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# O->ConBART document simplification system |
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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). |
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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. |
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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. |
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## How to use |
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It is recommended to use the [plan_simp](https://github.com/liamcripwell/plan_simp/tree/main) library to interface with the model. |
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Here is how to use this model in PyTorch: |
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```python |
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from plan_simp.models.bart import load_simplifier |
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simplifier, tokenizer, hparams = load_simplifier("liamcripwell/o-conbart") |
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# dynamic plan-guided generation |
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from plan_simp.scripts.generate import Launcher |
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launcher = Launcher() |
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launcher.dynamic(model_ckpt="liamcripwell/o-conbart", clf_model_ckpt="liamcripwell/pgdyn-plan", **params) |
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``` |
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Generation and evaluation can also be run from the terminal. |
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```bash |
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python plan_simp/scripts/generate.py dynamic |
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--clf_model_ckpt=liamcripwell/pgdyn-plan |
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--model_ckpt=liamcripwell/o-conbart |
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--test_file=<test_data> |
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--doc_id_col=pair_id |
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--context_dir=<context_dir> |
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--reading_lvl=s_level |
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--context_doc_id=c_id |
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--out_file=<output_csv> |
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python plan_simp/scripts/eval_simp.py |
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--input_data=newselaauto_docs_test.csv |
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--output_data=test_out_oconbart.csv |
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--x_col=complex_str |
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--r_col=simple_str |
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--y_col=pred |
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--doc_id_col=pair_id |
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--prepro=True |
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--sent_level=True |
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``` |
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