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: 0.1171
- Hard: {'precision': 0.7376681614349776, 'recall': 0.8225, 'f1': 0.7777777777777777, 'number': 800}
- Soft: {'precision': 0.7541899441340782, 'recall': 0.8709677419354839, 'f1': 0.8083832335329341, 'number': 155}
- Overall Precision: 0.7404
- Overall Recall: 0.8304
- Overall F1: 0.7828
- Overall Accuracy: 0.9675
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 | 158 | 0.1042 | {'precision': 0.6269592476489029, 'recall': 0.75, 'f1': 0.6829823562891292, 'number': 800} | {'precision': 0.6632124352331606, 'recall': 0.8258064516129032, 'f1': 0.735632183908046, 'number': 155} | 0.6330 | 0.7623 | 0.6917 | 0.9604 |
| No log | 2.0 | 316 | 0.0984 | {'precision': 0.6931567328918322, 'recall': 0.785, 'f1': 0.7362250879249707, 'number': 800} | {'precision': 0.6084905660377359, 'recall': 0.832258064516129, 'f1': 0.7029972752043598, 'number': 155} | 0.6771 | 0.7927 | 0.7303 | 0.9635 |
| No log | 3.0 | 474 | 0.1017 | {'precision': 0.7263513513513513, 'recall': 0.80625, 'f1': 0.764218009478673, 'number': 800} | {'precision': 0.696969696969697, 'recall': 0.8903225806451613, 'f1': 0.7818696883852692, 'number': 155} | 0.7210 | 0.8199 | 0.7673 | 0.9674 |
| 0.1083 | 4.0 | 632 | 0.1105 | {'precision': 0.7414187643020596, 'recall': 0.81, 'f1': 0.7741935483870969, 'number': 800} | {'precision': 0.7653631284916201, 'recall': 0.8838709677419355, 'f1': 0.8203592814371258, 'number': 155} | 0.7455 | 0.8220 | 0.7819 | 0.9685 |
| 0.1083 | 5.0 | 790 | 0.1171 | {'precision': 0.7376681614349776, 'recall': 0.8225, 'f1': 0.7777777777777777, 'number': 800} | {'precision': 0.7541899441340782, 'recall': 0.8709677419354839, 'f1': 0.8083832335329341, 'number': 155} | 0.7404 | 0.8304 | 0.7828 | 0.9675 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2