Create README.md

README.md

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+---
+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> 
+```