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--- |
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license: apache-2.0 |
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language: de |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wikiann |
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model-index: |
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- name: ner-bert-german |
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results: [] |
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examples: null |
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widget: |
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- text: "Herr Schmidt lebt in Berlin und arbeitet für die UN." |
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example_title: Schmidt aus Berlin |
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- text: "Die Deutsche Bahn hat ihren Hauptsitz in Frankfurt." |
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example_title: Deutsche Bahn |
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- text: "In München gibt es viele Unternehmen, z.B. BMW und Siemens." |
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example_title: München |
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metrics: |
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- seqeval |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ner-bert-german |
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This model can be used to do [named-entity recognition](https://en.wikipedia.org/wiki/Named-entity_recognition) in German. |
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It is trained on a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the German wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2450 |
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- Overall Precision: 0.8767 |
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- Overall Recall: 0.8893 |
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- Overall F1: 0.8829 |
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- Overall Accuracy: 0.9606 |
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- Loc F1: 0.9067 |
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- Org F1: 0.8278 |
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- Per F1: 0.9152 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:| |
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| 0.252 | 0.8 | 1000 | 0.1724 | 0.8422 | 0.8368 | 0.8395 | 0.9501 | 0.8702 | 0.7593 | 0.8921 | |
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| 0.1376 | 1.6 | 2000 | 0.1679 | 0.8388 | 0.8607 | 0.8497 | 0.9528 | 0.8814 | 0.7712 | 0.8971 | |
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| 0.0982 | 2.4 | 3000 | 0.1880 | 0.8631 | 0.8598 | 0.8614 | 0.9564 | 0.8847 | 0.7915 | 0.9070 | |
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| 0.0681 | 3.2 | 4000 | 0.1956 | 0.8599 | 0.8775 | 0.8686 | 0.9574 | 0.8905 | 0.8084 | 0.9097 | |
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| 0.0477 | 4.0 | 5000 | 0.2115 | 0.8738 | 0.8814 | 0.8776 | 0.9593 | 0.9003 | 0.8207 | 0.9144 | |
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| 0.031 | 4.8 | 6000 | 0.2274 | 0.8751 | 0.8826 | 0.8788 | 0.9598 | 0.9017 | 0.8246 | 0.9115 | |
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| 0.0229 | 5.6 | 7000 | 0.2317 | 0.8715 | 0.8888 | 0.8801 | 0.9598 | 0.9061 | 0.8208 | 0.9145 | |
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| 0.0181 | 6.4 | 8000 | 0.2450 | 0.8767 | 0.8893 | 0.8829 | 0.9606 | 0.9067 | 0.8278 | 0.9152 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
