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model card

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Kai Labusch 2023-01-31 10:02:35 +01:00
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doc/sbb_ner_model_card.md
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@ -87,12 +87,12 @@ It predicts the classes PER, LOC and ORG.
## Direct Use ## Direct Use
The model can directly be used to perform NER on historical german texts obtained by OCR from digitized documents.
Supported entity types are PER, LOC and ORG.
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- 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." --> <!-- 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." -->
## Downstream Use [Optional] ## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
@ -181,6 +181,7 @@ The evaluation focuses on NER in historical germans documents, see publication f
<!-- These are the evaluation metrics being used, ideally with a description of why. --> <!-- These are the evaluation metrics being used, ideally with a description of why. -->
Performance metrics used in evaluation is precision, recall and F1-score. Performance metrics used in evaluation is precision, recall and F1-score.
See paper for actual results in terms of these metrics.
## Results ## Results
@ -196,7 +197,7 @@ See publication.
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). 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).
- **Hardware Type:** V1 - **Hardware Type:** V100
- **Hours used:** Roughly 1-2 week(s) for pretraining. Roughly 1 hour for final NER-training. - **Hours used:** Roughly 1-2 week(s) for pretraining. Roughly 1 hour for final NER-training.
- **Cloud Provider:** No cloud. - **Cloud Provider:** No cloud.
- **Compute Region:** Germany. - **Compute Region:** Germany.