MikkoLipsanen
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README.md
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## Training data
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Some of the entities (for instance WORK_OF_ART, LAW, MONEY) that have been annotated in the [Turku OntoNotes Entities Corpus](https://github.com/TurkuNLP/turku-one)
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dataset were filtered out from the dataset used for training the model.
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entity classes contained in training, validation and test datasets are listed below:
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### Number of entity types in the data
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Val|1542|4042|108|1654|879|160|1858|177|257|299
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Test|1267|3698|86|1713|901|137|1843|174|233|260
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## Training procedure
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This model was trained using a NVIDIA RTX A6000 GPU with the following hyperparameters:
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- maximum length of data sequence: 512
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- patience: 2 epochs
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## Training data
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Some of the entities (for instance WORK_OF_ART, LAW, MONEY) that have been annotated in the [Turku OntoNotes Entities Corpus](https://github.com/TurkuNLP/turku-one)
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dataset were filtered out from the dataset used for training the model.
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In addition to this dataset, OCR'd and annotated content of
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digitized documents from Finnish public administration was also used for model training.
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The number of entities belonging to the different
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entity classes contained in training, validation and test datasets are listed below:
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### Number of entity types in the data
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Val|1542|4042|108|1654|879|160|1858|177|257|299
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Test|1267|3698|86|1713|901|137|1843|174|233|260
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The annotation of the data was performed as a cooperation between the National Archives of Finland
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and the [FIN-CLARIAH](https://www.kielipankki.fi/organization/fin-clariah/) research infrastructure
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for Social Sciences and Humanities.
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## Training procedure
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This model was trained using a NVIDIA RTX A6000 GPU with the following hyperparameters:
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- maximum length of data sequence: 512
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- patience: 2 epochs
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In the prerocessing stage, the input texts were split into chunks with a maximum length of 300 tokens,
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in order to avoid the tokenized chunks exceeding the maximum length of 512. Tokenization was performed
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using the tokenizer for the [bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1)
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model.
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The training code with instructions will be available soon [here](https://github.com/DALAI-hanke/BERT_NER).
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