File size: 2,334 Bytes
2525add 6186793 |
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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
---
language:
- en
tags:
- named-entity-disambiguation
- entity-disambiguation
- named-entity-linking
- entity-linking
- text2text-generation
- question-answering
- fill-mask
---
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) implemented in pytorch.
In a nutshell, GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned [BART](https://arxiv.org/abs/1910.13461) architecture. GENRE performs retrieval generating the unique entity name conditioned on the input text using constrained beam search to only generate valid identifiers. The model was first released in the [facebookresearch/GENRE](https://github.com/facebookresearch/GENRE) repository using `fairseq` (the `transformers` models are obtained with a conversion script similar to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py).
## BibTeX entry and citation info
**Please consider citing our works if you use code from this repository.**
```bibtex
@inproceedings{decao2020autoregressive,
title={Autoregressive Entity Retrieval},
author={Nicola {De Cao} and Gautier Izacard and Sebastian Riedel and Fabio Petroni},
booktitle={International Conference on Learning Representations},
url={https://openreview.net/forum?id=5k8F6UU39V},
year={2021}
}
```
## Usage
Here is an example of generation for Wikipedia page retrieval for open-domain fact-checking:
```python
import pickle
from trie import Trie
from transformers import BartTokenizer, BartForConditionalGeneration
# OPTIONAL: load the prefix tree (trie)
# with open("kilt_titles_trie_dict.pkl", "rb") as f:
# trie = Trie.load_from_dict(pickle.load(f))
tokenizer = BartTokenizer.from_pretrained("facebook/genre-kilt")
model = BartForConditionalGeneration.from_pretrained("facebook/genre-kilt").eval()
sentences = ["Einstein was a German physicist."]
outputs = model.generate(
**tokenizer(sentences, return_tensors="pt"),
num_beams=5,
num_return_sequences=5,
# OPTIONAL: use constrained beam search
# prefix_allowed_tokens_fn=lambda batch_id, sent: trie.get(sent.tolist()),
)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
``` |