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--- |
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language: |
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- ger |
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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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- f1 |
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model-index: |
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- name: th-nuernberg/gbert-large-german-counseling-gecco |
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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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# th-nuernberg/gbert-large-german-counseling-gecco |
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This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) |
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trained with the German E-Counseling Conversation Dataset, |
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created at the Technische Hochschule Nürnberg (see [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: Accuracy 0.78, F1 0.66. |
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Contact: |
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- [Prof. Dr. Jens Albrecht](https://www.th-nuernberg.de/person/albrecht-jens/) |
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- [Prof. Dr. Robert Lehmann](https://www.th-nuernberg.de/person/lehmann-robert/) |
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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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| 3.3924 | 1.0 | 20 | 2.9410 | 0.2032 | 0.0418 | |
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| 2.7028 | 2.0 | 40 | 2.2499 | 0.4806 | 0.2366 | |
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| 2.0665 | 3.0 | 60 | 1.7404 | 0.6129 | 0.3537 | |
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| 1.5 | 4.0 | 80 | 1.3602 | 0.6839 | 0.4109 | |
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| 1.0794 | 5.0 | 100 | 1.1377 | 0.7355 | 0.4971 | |
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| 0.7965 | 6.0 | 120 | 1.0123 | 0.7548 | 0.5518 | |
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| 0.6438 | 7.0 | 140 | 0.9806 | 0.7613 | 0.5547 | |
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| 0.5039 | 8.0 | 160 | 0.9452 | 0.7742 | 0.6019 | |
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| 0.4058 | 9.0 | 180 | 0.9218 | 0.7774 | 0.5907 | |
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| 0.3363 | 10.0 | 200 | 0.9373 | 0.7710 | 0.6157 | |
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| 0.2451 | 11.0 | 220 | 0.9751 | 0.7548 | 0.5955 | |
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| 0.1997 | 12.0 | 240 | 0.9197 | 0.7839 | 0.6526 | |
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| 0.1765 | 13.0 | 260 | 0.9187 | 0.7806 | 0.6425 | |
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| 0.1453 | 14.0 | 280 | 0.9431 | 0.7742 | 0.6357 | |
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| 0.1216 | 15.0 | 300 | 0.9388 | 0.7839 | 0.6534 | |
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| 0.1097 | 16.0 | 320 | 0.9290 | 0.7839 | 0.6645 | |
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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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