metadata
license: mit
base_model: bert-base-german-cased
tags:
- generated_from_trainer
model-index:
- name: gerskill-bert
results: []
gerskill-bert
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1187
- Hard: {'precision': 0.7063339731285988, 'recall': 0.8070175438596491, 'f1': 0.7533265097236438, 'number': 456}
- Soft: {'precision': 0.7111111111111111, 'recall': 0.7804878048780488, 'f1': 0.7441860465116279, 'number': 82}
- Overall Precision: 0.7070
- Overall Recall: 0.8030
- Overall F1: 0.7520
- Overall Accuracy: 0.9644
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Hard | Soft | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 178 | 0.1433 | {'precision': 0.5503355704697986, 'recall': 0.7192982456140351, 'f1': 0.6235741444866921, 'number': 456} | {'precision': 0.5137614678899083, 'recall': 0.6829268292682927, 'f1': 0.5863874345549738, 'number': 82} | 0.5447 | 0.7138 | 0.6179 | 0.9448 |
| No log | 2.0 | 356 | 0.1181 | {'precision': 0.7031578947368421, 'recall': 0.7324561403508771, 'f1': 0.7175080558539205, 'number': 456} | {'precision': 0.6585365853658537, 'recall': 0.6585365853658537, 'f1': 0.6585365853658537, 'number': 82} | 0.6966 | 0.7212 | 0.7087 | 0.9544 |
| 0.1645 | 3.0 | 534 | 0.1079 | {'precision': 0.6605839416058394, 'recall': 0.793859649122807, 'f1': 0.7211155378486056, 'number': 456} | {'precision': 0.6966292134831461, 'recall': 0.7560975609756098, 'f1': 0.7251461988304092, 'number': 82} | 0.6656 | 0.7881 | 0.7217 | 0.9603 |
| 0.1645 | 4.0 | 712 | 0.1146 | {'precision': 0.7030651340996169, 'recall': 0.8048245614035088, 'f1': 0.7505112474437627, 'number': 456} | {'precision': 0.6739130434782609, 'recall': 0.7560975609756098, 'f1': 0.7126436781609194, 'number': 82} | 0.6987 | 0.7974 | 0.7448 | 0.9621 |
| 0.1645 | 5.0 | 890 | 0.1187 | {'precision': 0.7063339731285988, 'recall': 0.8070175438596491, 'f1': 0.7533265097236438, 'number': 456} | {'precision': 0.7111111111111111, 'recall': 0.7804878048780488, 'f1': 0.7441860465116279, 'number': 82} | 0.7070 | 0.8030 | 0.7520 | 0.9644 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2