| --- |
| language: |
| - en |
| library_name: transformers |
| pipeline_tag: other |
| base_model: google-t5/t5-large |
| base_model_relation: finetune |
| tags: |
| - text-to-graph |
| - webnlg |
| - regen |
| --- |
| |
| # ReGen T2G.CE for WebNLG |
|
|
| This T5-large model converts English text into subject–relation–object triples. It implements the WebNLG T2G.CE setting of [Dognin et al. (2021)](https://aclanthology.org/2021.emnlp-main.83/), using cross-entropy fine-tuning. |
|
|
| The first author of the [XQDT paper](https://huggingface.co/papers/2608.29948) reproduced ReGen T2G.CE for use as a base text-to-data model in that study. The [reproduction repository](https://github.com/guihuzhang/bt5-regen-text-to-graph) contains the code, saved predictions, and evaluation scripts. |
|
|
| The checkpoint is epoch 3 of the continuation run. Its saved predictions on 2,155 WebNLG 2020 English test examples give Exact precision 70.35, recall 72.18, and F1 71.07, using five-beam decoding. For CE on the same task, [Table 2 of the ReGen paper](https://aclanthology.org/2021.emnlp-main.83.pdf#page=7) reports 72.3 Exact F1, while the example run in the [IBM training guide](https://github.com/IBM/regen/blob/main/docs/train_t2g_webnlg.md#overview) scores 70.11. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from transformers import T5ForConditionalGeneration, T5Tokenizer |
| |
| model_path = "Loria-MosAIk/regen-webnlg-t2g-ce" |
| tokenizer = T5Tokenizer.from_pretrained(model_path, legacy=True) |
| model = T5ForConditionalGeneration.from_pretrained(model_path).eval() |
| inputs = tokenizer("Text to Graph: Lina Dorado was born in Bogotá in 1975.", return_tensors="pt") |
| prefix = torch.full((1, 1), tokenizer.pad_token_id, dtype=inputs.input_ids.dtype) |
| input_ids = torch.cat((prefix, inputs.input_ids), dim=1) |
| attention_mask = torch.cat((torch.ones_like(prefix), inputs.attention_mask), dim=1) |
| outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, num_beams=5, max_length=256) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)) |
| ``` |
|
|
| Output: |
|
|
| ```text |
| __subject__ Lina_Dorado __predicate__ birthPlace __object__ Bogotá __subject__ Lina_Dorado __predicate__ birthYear __object__ 1975 |
| ``` |
|
|
| The example prepends a pad token to match ReGen's data loader. |
|
|
| The [base T5-large model](https://huggingface.co/google-t5/t5-large) is distributed under Apache-2.0. The WebNLG fine-tuning data are distributed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
|
|
| ## Citation |
|
|
| Original ReGen model: [Dognin et al. (2021)](https://arxiv.org/abs/2108.12472). |
|
|
| ```bibtex |
| @inproceedings{dognin-etal-2021-regen, |
| title = "{R}e{G}en: {R}einforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models", |
| author = "Dognin, Pierre and |
| Padhi, Inkit and |
| Melnyk, Igor and |
| Das, Payel", |
| editor = "Moens, Marie-Francine and |
| Huang, Xuanjing and |
| Specia, Lucia and |
| Yih, Scott Wen-tau", |
| booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
| month = nov, |
| year = "2021", |
| address = "Online and Punta Cana, Dominican Republic", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2021.emnlp-main.83/", |
| doi = "10.18653/v1/2021.emnlp-main.83", |
| pages = "1084--1099" |
| } |
| ``` |
|
|
| This checkpoint and reproduction code: [Efimov-Zhang et al. (2026)](https://aclanthology.org/2026.emnlp-main.397/). |
|
|
| ```bibtex |
| @inproceedings{efimov-zhang-etal-2026-xqdt, |
| title = "{XQDT}: e{X}plainable and Quantitative Data-Text Alignment Metric with Feedback Signals", |
| author = "Efimov-Zhang, Kun and |
| Song, Yifei and |
| Gardent, Claire", |
| editor = "Lee, Hung-Yi and |
| Dev, Sunipa and |
| Li, Jessy and |
| Elliott, Desmond", |
| booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing", |
| month = oct, |
| year = "2026", |
| address = "Budapest, Hungary", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2026.emnlp-main.397/", |
| doi = "10.18653/2026.emnlp-main.397", |
| pages = "9121--9157", |
| ISBN = "979-8-89176-459-0" |
| } |
| ``` |
|
|
