metadata
license: mit
base_model: dathi103/bert-job-german-extended
tags:
- generated_from_trainer
model-index:
- name: gerskill-bert-job-extended
results: []
gerskill-bert-job-extended
This model is a fine-tuned version of dathi103/bert-job-german-extended on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1440
- Hard: {'precision': 0.7093596059113301, 'recall': 0.7933884297520661, 'f1': 0.7490247074122237, 'number': 363}
- Soft: {'precision': 0.7058823529411765, 'recall': 0.7272727272727273, 'f1': 0.7164179104477613, 'number': 66}
- Overall Precision: 0.7089
- Overall Recall: 0.7832
- Overall F1: 0.7442
- Overall Accuracy: 0.9650
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 | 0.1035 | {'precision': 0.6582278481012658, 'recall': 0.7162534435261708, 'f1': 0.6860158311345645, 'number': 363} | {'precision': 0.6451612903225806, 'recall': 0.6060606060606061, 'f1': 0.625, 'number': 66} | 0.6565 | 0.6993 | 0.6772 | 0.9597 |
| No log | 2.0 | 356 | 0.1067 | {'precision': 0.6641414141414141, 'recall': 0.7245179063360881, 'f1': 0.6930171277997365, 'number': 363} | {'precision': 0.676923076923077, 'recall': 0.6666666666666666, 'f1': 0.6717557251908397, 'number': 66} | 0.6659 | 0.7156 | 0.6899 | 0.9634 |
| 0.1072 | 3.0 | 534 | 0.1204 | {'precision': 0.7079207920792079, 'recall': 0.7878787878787878, 'f1': 0.7457627118644068, 'number': 363} | {'precision': 0.6956521739130435, 'recall': 0.7272727272727273, 'f1': 0.711111111111111, 'number': 66} | 0.7061 | 0.7786 | 0.7406 | 0.9652 |
| 0.1072 | 4.0 | 712 | 0.1350 | {'precision': 0.7178841309823678, 'recall': 0.7851239669421488, 'f1': 0.7500000000000001, 'number': 363} | {'precision': 0.6956521739130435, 'recall': 0.7272727272727273, 'f1': 0.711111111111111, 'number': 66} | 0.7146 | 0.7762 | 0.7441 | 0.9644 |
| 0.1072 | 5.0 | 890 | 0.1440 | {'precision': 0.7093596059113301, 'recall': 0.7933884297520661, 'f1': 0.7490247074122237, 'number': 363} | {'precision': 0.7058823529411765, 'recall': 0.7272727272727273, 'f1': 0.7164179104477613, 'number': 66} | 0.7089 | 0.7832 | 0.7442 | 0.9650 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2