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--- |
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license: mit |
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base_model: dbmdz/bert-base-german-uncased |
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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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- f1 |
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model-index: |
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- name: gecco-bert-base-german-uncased |
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results: [] |
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widget: |
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- text: "Was haben Sie bisher unternommen, um ihr Problem zu lösen?" |
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- text: "Hallo Peter, wie kann ich helfen?" |
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- text: "Ich bin hier, um zuzuhören. Wenn du mir erzählen möchtest, wie es dir geht, bin ich bereit." |
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- text: "Fällt es dir leicht, mit anderen Menschen in Kontakt zu treten?" |
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- text: "Welche Hobbys oder Freizeitaktivitäten würdest du gerne in der Zukunft ausprobieren?" |
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- text: "Haben Sie finanzielle Unterstützung von Ihrem Mann?" |
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- text: "Könnten Sie bitte genauer beschreiben, welche Schwierigkeiten durch diese technischen Probleme entstehen?" |
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- text: "Gibt es denn keine Hobbys, die du mit deinen Freunden gemeinsam machen kannst?" |
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- text: "Wo geht ihr Sohn zur Schule?" |
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- text: "Haben sie gemeinsame Hobbies mit Ihren Freunden?" |
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--- |
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# gecco-bert-base-german-uncased |
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This model is for text classfication of German counseling messages. |
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It is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) |
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trained with the German E-Counseling Conversation Dataset, |
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created at the Technische Hochschule Nürnberg: [github.com/th-nuernberg/gecco-dataset](https://github.com/th-nuernberg/gecco-dataset) |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2341 |
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- Accuracy: 0.6968 |
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- F1: 0.4493 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 16 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 3.4151 | 1.0 | 20 | 3.0885 | 0.2935 | 0.0760 | |
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| 2.9316 | 2.0 | 40 | 2.7003 | 0.3484 | 0.1035 | |
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| 2.5556 | 3.0 | 60 | 2.3463 | 0.5032 | 0.2350 | |
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| 2.19 | 4.0 | 80 | 2.0714 | 0.5613 | 0.2841 | |
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| 1.904 | 5.0 | 100 | 1.8381 | 0.6 | 0.3085 | |
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| 1.6285 | 6.0 | 120 | 1.6712 | 0.6323 | 0.3633 | |
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| 1.4482 | 7.0 | 140 | 1.5518 | 0.6581 | 0.3774 | |
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| 1.2807 | 8.0 | 160 | 1.4796 | 0.6677 | 0.3880 | |
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| 1.1126 | 9.0 | 180 | 1.4207 | 0.6613 | 0.3787 | |
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| 1.0747 | 10.0 | 200 | 1.3461 | 0.6774 | 0.3885 | |
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| 0.9068 | 11.0 | 220 | 1.3097 | 0.6871 | 0.4132 | |
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| 0.8498 | 12.0 | 240 | 1.2893 | 0.6903 | 0.4235 | |
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| 0.8343 | 13.0 | 260 | 1.2549 | 0.7 | 0.4332 | |
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| 0.7375 | 14.0 | 280 | 1.2426 | 0.7 | 0.4497 | |
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| 0.7274 | 15.0 | 300 | 1.2385 | 0.7 | 0.4512 | |
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| 0.6916 | 16.0 | 320 | 1.2341 | 0.6968 | 0.4493 | |
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### Framework versions |
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- Transformers 4.35.1 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 2.14.7 |
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- Tokenizers 0.14.1 |
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