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language: |
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- de |
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license: mit |
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widget: |
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- text: | |
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Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um künstliche Intelligenz durch Open Source und Open Science zu demokratisieren. |
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datasets: |
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- germaner |
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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: gbert-base-germaner |
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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: germaner |
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type: germaner |
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args: default |
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metrics: |
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- name: precision |
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type: precision |
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value: 0.8520523797532108 |
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- name: recall |
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type: recall |
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value: 0.8754204398447607 |
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- name: f1 |
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type: f1 |
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value: 0.8635783563042368 |
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- name: accuracy |
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type: accuracy |
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value: 0.976147969774973 |
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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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# gbert-base-germaner |
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This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the germaner dataset. |
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It achieves the following results on the evaluation set: |
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- precision: 0.8521 |
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- recall: 0.8754 |
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- f1: 0.8636 |
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- accuracy: 0.9761 |
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If you want to learn how to fine-tune BERT yourself using Keras and Tensorflow check out this blog post: |
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https://www.philschmid.de/huggingface-transformers-keras-tf |
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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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- num_train_epochs: 5 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- learning_rate: 2e-05 |
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- weight_decay_rate: 0.01 |
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- num_warmup_steps: 0 |
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- fp16: True |
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### Framework versions |
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- Transformers 4.14.1 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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