Historical Newspaper Ner

🚀 Historical newspaper NER

A fine - tuned Roberta - large model for token classification on text with potential OCR errors, trained to recognize multiple entity types.

🚀 Quick Start

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)

✨ Features

• Fine - tuned Roberta - large: The historical_newspaper_ner is a fine - tuned Roberta - large model suitable for text that may contain OCR errors.
• Entity recognition: It can recognize four types of entities: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC).
• High - quality training data: Trained on a custom historical newspaper dataset with highly accurate labels, double - entered by two skilled Harvard undergraduates and discrepancies resolved manually.

📚 Documentation

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, as shown in the code example above.

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 settings. 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:

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

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

Notes

This model card was influenced by that of [dslim/bert - base - NER](https://huggingface.co/dslim/bert - base - NER/edit/main/README.md)

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](https://huggingface.co/datasets/dell - research - harvard/newswire).

📄 License

This model is licensed under CC - BY - 2.0.