File size: 1,708 Bytes
916ff14 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
language:
- en
---
# pgdyn-plan
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".
It is the be used in conjunction with [the planning component](https://huggingface.co/liamcripwell/pgdyn-plan) 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>
```
|