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--- |
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license: mit |
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base_model: deepset/gbert-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: FragZONFactMetaRecommendation |
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results: [] |
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widget: |
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- text: "Wer arbeitet im Ressort PWG von ZEIT ONLINE?" |
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example_title: "Meta" |
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- text: "Wann ist Helmut Schmidt gestorben?" |
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example_title: "Fakt" |
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- text: "Was kann die ZEIT-KI?" |
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example_title: "Meta" |
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- text: "Wer hat 2021/22 die Meisterschaft der Fussballbundesliga gewonnen?" |
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example_title: "Fakt" |
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- text: "Wen soll ich bei der nächsten Bundestagswahl wählen?" |
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example_title: "Empfehlung" |
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--- |
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# FragZONFactMetaRecommendation |
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This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the beta dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4128 |
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- Accuracy: 0.9205 |
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## Model description |
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Fine-tuned gbert on queries |
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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: 5e-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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.171 | 1.0 | 76 | 0.4003 | 0.9139 | |
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| 0.1154 | 2.0 | 152 | 0.4128 | 0.9205 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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