Create README.md

README.md

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+---
+language:
+- en
+---
+
+# O->LED_para 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 the [Longformer encoder-decoder](https://huggingface.co/allenai/led-base-16384) that operates at the paragraph-level and is intended to be guided by a 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 load this model in PyTorch:
+
+```python
+from plan_simp.models.bart import load_simplifier
+
+simplifier, tokenizer, hparams = load_simplifier("liamcripwell/o-ledpara")
+```
+
+Plan-guided generation and evaluation can be run from the terminal (see the repo for more details).
+
+```bash
+python doc_simp/scripts/generate.py dynamic
+  --clf_model_ckpt=liamcripwell/pgdyn-plan
+  --model_ckpt=liamcripwell/o-ledpara
+  --test_file=<test_data>
+  --doc_id_col=pair_id
+  --context_dir=<context_dir>
+  --out_file=<output_csv>
+  --reading_lvl=s_level
+  --context_doc_id=pair_id
+  --para_lvl=True
+
+python plan_simp/scripts/eval_simp.py
+    --input_data=newselaauto_docs_test.csv
+    --output_data=test_out_oledpara.csv
+    --x_col=complex_str
+    --r_col=simple_str
+    --y_col=pred
+    --doc_id_col=pair_id
+    --prepro=True
+    --sent_level=True
+```