Downloads · 30 days
10
2% of all-time downloads
gbert-base-german-counseling-gecco
th-nuernberg/gbert-base-german-counseling-gecco
gbert-base-german-counseling-gecco is a text classification model from th-nuernberg. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
426
Public
Parameters
110M
440 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors440 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Nov 29, 2023
- Updated
- Nov 29, 2023
- SHA
- 33139d53
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
gecco-german-counseling-gbert-base
This model is a fine-tuned version of deepset/gbert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2480
- Accuracy: 0.7194
- F1: 0.5062
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 3.439 | 1.0 | 20 | 3.1181 | 0.2484 | 0.0474 |
| 2.9431 | 2.0 | 40 | 2.6841 | 0.3935 | 0.1637 |
| 2.5477 | 3.0 | 60 | 2.3120 | 0.5387 | 0.2802 |
| 2.1823 | 4.0 | 80 | 2.0526 | 0.5935 | 0.3138 |
| 1.8786 | 5.0 | 100 | 1.8242 | 0.6387 | 0.3541 |
| 1.6267 | 6.0 | 120 | 1.6720 | 0.6548 | 0.3682 |
| 1.4447 | 7.0 | 140 | 1.5538 | 0.6645 | 0.3718 |
| 1.2734 | 8.0 | 160 | 1.4655 | 0.6710 | 0.3801 |
| 1.1099 | 9.0 | 180 | 1.4040 | 0.6935 | 0.4202 |
| 1.0766 | 10.0 | 200 | 1.3541 | 0.6903 | 0.4330 |
| 0.913 | 11.0 | 220 | 1.3078 | 0.6968 | 0.4629 |
| 0.8557 | 12.0 | 240 | 1.2879 | 0.7161 | 0.5000 |
| 0.8477 | 13.0 | 260 | 1.2772 | 0.7097 | 0.4946 |
| 0.7412 | 14.0 | 280 | 1.2598 | 0.7161 | 0.5042 |
| 0.7341 | 15.0 | 300 | 1.2484 | 0.7194 | 0.5069 |
| 0.7029 | 16.0 | 320 | 1.2480 | 0.7194 | 0.5062 |
Framework versions
- Transformers 4.35.1
- Pytorch 1.10.1+cu111
- Datasets 2.14.7
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Nov 29, 2023
- Updated
- Nov 29, 2023