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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-german-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8333588604686782 |
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- name: Recall |
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type: recall |
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value: 0.8620088719898605 |
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- name: F1 |
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type: f1 |
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value: 0.8474417880227396 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9292245320451997 |
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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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# bert-german-ner |
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This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3196 |
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- Precision: 0.8334 |
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- Recall: 0.8620 |
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- F1: 0.8474 |
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- Accuracy: 0.9292 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 300 | 0.3617 | 0.7310 | 0.7733 | 0.7516 | 0.8908 | |
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| 0.5428 | 2.0 | 600 | 0.2897 | 0.7789 | 0.8395 | 0.8081 | 0.9132 | |
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| 0.5428 | 3.0 | 900 | 0.2805 | 0.8147 | 0.8465 | 0.8303 | 0.9221 | |
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| 0.2019 | 4.0 | 1200 | 0.2816 | 0.8259 | 0.8498 | 0.8377 | 0.9260 | |
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| 0.1215 | 5.0 | 1500 | 0.2942 | 0.8332 | 0.8599 | 0.8463 | 0.9285 | |
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| 0.1215 | 6.0 | 1800 | 0.3053 | 0.8293 | 0.8619 | 0.8452 | 0.9287 | |
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| 0.0814 | 7.0 | 2100 | 0.3190 | 0.8249 | 0.8634 | 0.8437 | 0.9267 | |
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| 0.0814 | 8.0 | 2400 | 0.3196 | 0.8334 | 0.8620 | 0.8474 | 0.9292 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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