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model card
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@ -87,12 +87,12 @@ It predicts the classes PER, LOC and ORG.
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## Direct Use
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## Direct Use
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The model can directly be used to perform NER on historical german texts obtained by OCR from digitized documents.
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Supported entity types are PER, LOC and ORG.
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
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## Downstream Use [Optional]
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## Downstream Use [Optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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@ -181,6 +181,7 @@ The evaluation focuses on NER in historical germans documents, see publication f
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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Performance metrics used in evaluation is precision, recall and F1-score.
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Performance metrics used in evaluation is precision, recall and F1-score.
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See paper for actual results in terms of these metrics.
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## Results
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## Results
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@ -196,7 +197,7 @@ See publication.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** V1
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- **Hardware Type:** V100
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- **Hours used:** Roughly 1-2 week(s) for pretraining. Roughly 1 hour for final NER-training.
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- **Hours used:** Roughly 1-2 week(s) for pretraining. Roughly 1 hour for final NER-training.
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- **Cloud Provider:** No cloud.
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- **Cloud Provider:** No cloud.
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- **Compute Region:** Germany.
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- **Compute Region:** Germany.
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