Historical newspaper NER
Model description
historical_newspaper_ner is a fine-tuned Roberta-large model for use on text that may contain OCR errors.
It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).
It was trained on a custom historical newspaper dataset, with highly accurate labels. All data were double entered by two highly skilled Harvard undergraduates and all discrepancies were resolved by hand.
Intended uses
You can use this model with Transformers pipeline for NER.
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("dell-research-harvard/historical_newspaper_ner")
model = AutoModelForTokenClassification.from_pretrained("dell-research-harvard/historical_newspaper_ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"
ner_results = nlp(example)
print(ner_results)
Limitations and bias
This model was trained on historical news and may reflect biases from a specific period of time. It may also not generalise well to other setting. Additionally, the model occasionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.
Training data
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. Each token will be classified as one of the following classes:
| Abbreviation | Description |
|---|---|
| O | Outside of a named entity |
| B-MISC | Beginning of a miscellaneous entity |
| I-MISC | Miscellaneous entity |
| B-PER | Beginning of a person’s name |
| I-PER | Person’s name |
| B-ORG | Beginning of an organization |
| I-ORG | organization |
| B-LOC | Beginning of a location |
| I-LOC | Location |
This model was fine-tuned on historical English-language news that had been OCRd from American newspapers. Unlike other NER datasets, this data has highly accurate labels. All data were double entered by two highly skilled Harvard undergraduates and all discrepancies were resolved by hand.
# of training examples per entity type
| Dataset | Article | PER | ORG | LOC | MISC |
|---|---|---|---|---|---|
| Train | 227 | 1345 | 450 | 1191 | 1037 |
| Dev | 48 | 231 | 59 | 192 | 149 |
| Test | 48 | 261 | 83 | 199 | 181 |
Training procedure
The data was used to fine-tune a Roberta-Large model (Liu et. al, 2020) at a learning rate of 4.7e-05 with a batch size of 128 for 184 epochs.
Eval results
| entities | f1 |
|---|---|
| PER | 94.3 |
| ORG | 80.7 |
| LOC | 90.8 |
| MISC | 79.6 |
| Overall (stringent) | 86.5 |
| Overall (ignoring entity type) | 90.4 |
Notes
This model card was influence by that of dslim/bert-base-NER
Citation
If you use this model, you can cite the following paper:
@misc{franklin2024ndjv,
title={News Deja Vu: Connecting Past and Present with Semantic Search},
author={Brevin Franklin, Emily Silcock, Abhishek Arora, Tom Bryan and Melissa Dell},
year={2024},
eprint={2406.15593},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.15593},
}
Applications
We applied this model to a century of historical news articles. You can see all the named entities in the NEWSWIRE dataset.