metadata
language:
- en
inference: false
tags:
- BERT
- BNC-BERT
- encoder
license: cc-by-4.0
BNC-BERT
- Paper: Trained on 100 million words and still in shape: BERT meets British National Corpus
- GitHub: ltgoslo/ltg-bert
Example usage
This model currently needs a custom wrapper from modeling_ltgbert.py. Then you can use it like this:
import torch
from transformers import AutoTokenizer
from modeling_ltgbert import LtgBertForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("path/to/folder")
bert = LtgBertForMaskedLM.from_pretrained("path/to/folder")
Please cite the following publication (just arXiv for now)
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
pages = "1954--1974",
abstract = "While modern masked language models (LMs) are trained on ever larger corpora, we here explore the effects of down-scaling training to a modestly-sized but representative, well-balanced, and publicly available English text source {--} the British National Corpus. We show that pre-training on this carefully curated corpus can reach better performance than the original BERT model. We argue that this type of corpora has great potential as a language modeling benchmark. To showcase this potential, we present fair, reproducible and data-efficient comparative studies of LMs, in which we evaluate several training objectives and model architectures and replicate previous empirical results in a systematic way. We propose an optimized LM architecture called LTG-BERT.",
}