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---
license: mit
base_model: deepset/gbert-base
tags:
- generated_from_trainer
model-index:
- name: gerskill-gbert
results: []
---
<!-- 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. -->
# gerskill-gbert
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1095
- Hard: {'precision': 0.7445544554455445, 'recall': 0.8245614035087719, 'f1': 0.7825182101977106, 'number': 456}
- Soft: {'precision': 0.7272727272727273, 'recall': 0.7804878048780488, 'f1': 0.7529411764705882, 'number': 82}
- Overall Precision: 0.7420
- Overall Recall: 0.8178
- Overall F1: 0.7781
- Overall Accuracy: 0.9635
## 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 | 0.1255 | {'precision': 0.583756345177665, 'recall': 0.756578947368421, 'f1': 0.659025787965616, 'number': 456} | {'precision': 0.5425531914893617, 'recall': 0.6219512195121951, 'f1': 0.5795454545454546, 'number': 82} | 0.5781 | 0.7361 | 0.6476 | 0.9515 |
| No log | 2.0 | 356 | 0.1071 | {'precision': 0.6994219653179191, 'recall': 0.7960526315789473, 'f1': 0.7446153846153847, 'number': 456} | {'precision': 0.6129032258064516, 'recall': 0.6951219512195121, 'f1': 0.6514285714285714, 'number': 82} | 0.6863 | 0.7807 | 0.7304 | 0.9585 |
| 0.1562 | 3.0 | 534 | 0.0990 | {'precision': 0.7150943396226415, 'recall': 0.831140350877193, 'f1': 0.7687626774847871, 'number': 456} | {'precision': 0.6777777777777778, 'recall': 0.7439024390243902, 'f1': 0.7093023255813954, 'number': 82} | 0.7097 | 0.8178 | 0.7599 | 0.9621 |
| 0.1562 | 4.0 | 712 | 0.1072 | {'precision': 0.7258687258687259, 'recall': 0.8245614035087719, 'f1': 0.7720739219712526, 'number': 456} | {'precision': 0.7222222222222222, 'recall': 0.7926829268292683, 'f1': 0.7558139534883721, 'number': 82} | 0.7253 | 0.8197 | 0.7696 | 0.9628 |
| 0.1562 | 5.0 | 890 | 0.1095 | {'precision': 0.7445544554455445, 'recall': 0.8245614035087719, 'f1': 0.7825182101977106, 'number': 456} | {'precision': 0.7272727272727273, 'recall': 0.7804878048780488, 'f1': 0.7529411764705882, 'number': 82} | 0.7420 | 0.8178 | 0.7781 | 0.9635 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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