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language: |
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- en |
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# pgdyn-plan |
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This is a pretrained model for the simplification component of the PG_Dyn system, described in the EACL 2023 paper "Document-Level Planning for Text Simplification". |
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It is the be used in conjunction with [the planning component](https://huggingface.co/liamcripwell/pgdyn-plan) to form the full pipeline. |
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The code [in this repo](https://github.com/liamcripwell/plan_simp) should be used. |
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## How to use |
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Here is how to load this model in PyTorch: |
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```python |
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from plan_simp.models.classifier import load_planner |
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from plan_simp.models.bart import load_simplifier |
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# contextual simplification planner |
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planner, p_tokenizer, p_hparams = load_planner("liamcripwell/pgdyn-plan") |
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# simplification model |
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simplifier, tokenizer, hparams = load_simplifier("liamcripwell/pgdyn-simp") |
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``` |
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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. |
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```bash |
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# using planner |
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python plan_simp/scripts/generate.py dynamic |
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--clf_model_ckpt=<planner_model> # e.g. liamcripwell/pgdyn-plan |
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--model_ckpt=<simplification_model> # e.g. liamcripwell/pgdyn-simp |
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--test_file=<test_sentences> |
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--doc_id_col=pair_id # document identifier for each sentence |
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--context_doc_id=c_id |
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--context_dir=<context_dir> |
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--reading_lvl=s_level |
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--out_file=<output_csv> |
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# manual specification of operations (no planner) |
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python plan_simp/scripts/generate.py inference |
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--model_ckpt=<simplification_model> # e.g. liamcripwell/pgdyn-simp |
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--test_file=<test_sentences> |
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--op_col=label |
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--reading_lvl=s_level |
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--out_file=<output_csv> |
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``` |
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