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--- |
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license: cc-by-2.0 |
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language: |
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- en |
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pipeline_tag: token-classification |
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--- |
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# Historical newspaper NER |
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## Model description |
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**historical_newspaper_ner** is a fine-tuned Roberta-large model for use on text that may contain OCR errors. |
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It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). |
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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. |
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## Intended uses |
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You can use this model with Transformers pipeline for NER. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("dell-research-harvard/historical_newspaper_ner") |
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model = AutoModelForTokenClassification.from_pretrained("dell-research-harvard/historical_newspaper_ner") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "My name is Wolfgang and I live in Berlin" |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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## Limitations and bias |
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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. |
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Additionally, the model occasionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. |
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## Training data |
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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: |
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Abbreviation|Description |
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-|- |
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O|Outside of a named entity |
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B-MISC |Beginning of a miscellaneous entity |
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I-MISC | Miscellaneous entity |
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B-PER |Beginning of a person’s name |
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I-PER |Person’s name |
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B-ORG |Beginning of an organization |
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I-ORG |organization |
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B-LOC |Beginning of a location |
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I-LOC |Location |
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This model was fine-tuned on historical English-language news that had been OCRd from American newspapers. |
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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. |
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#### # of training examples per entity type |
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Dataset|Article|PER|ORG|LOC|MISC |
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-|-|-|-|-|- |
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Train|227|1345|450|1191|1037 |
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Dev|48|231|59|192|149 |
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Test|48|261|83|199|181 |
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## Training procedure |
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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. |
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## Eval results |
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entities|f1 |
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-|- |
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PER | 94.3 |
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ORG | 80.7 |
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LOC | 90.8 |
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MISC | 79.6 |
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Overall (stringent) | 86.5 |
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Overall (ignoring entity type) | 90.4 |
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## Notes |
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This model card was influence by that of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER/edit/main/README.md) |
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## Citation |
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If you use this model, you can cite the following paper: |
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``` |
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@misc{franklin2024ndjv, |
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title={News Deja Vu: Connecting Past and Present with Semantic Search}, |
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author={Brevin Franklin, Emily Silcock, Abhishek Arora, Tom Bryan and Melissa Dell}, |
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year={2024}, |
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eprint={2406.15593}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2406.15593}, |
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} |
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``` |
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## Applications |
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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). |
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