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---
library_name: transformers
base_model: moritzbur/lilt-GottBERT-base
tags:
- generated_from_trainer
datasets:
- xfund
model-index:
- name: lilt-GottBERT-base-xfund-de
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. -->
# lilt-GottBERT-base-xfund-de
This model is a fine-tuned version of [moritzbur/lilt-GottBERT-base](https://huggingface.co/moritzbur/lilt-GottBERT-base) on the xfund dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0170
- Answer: {'precision': 0.8059836808703535, 'recall': 0.8193548387096774, 'f1': 0.8126142595978061, 'number': 1085}
- Header: {'precision': 0.6590909090909091, 'recall': 0.5, 'f1': 0.5686274509803921, 'number': 58}
- Question: {'precision': 0.7037037037037037, 'recall': 0.837465564738292, 'f1': 0.7647798742138364, 'number': 726}
- Overall Precision: 0.7588
- Overall Recall: 0.8165
- Overall F1: 0.7866
- Overall Accuracy: 0.7433
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.6294 | 10.5263 | 200 | 1.2270 | {'precision': 0.7253032928942807, 'recall': 0.7714285714285715, 'f1': 0.7476552032157213, 'number': 1085} | {'precision': 0.4117647058823529, 'recall': 0.4827586206896552, 'f1': 0.4444444444444445, 'number': 58} | {'precision': 0.6296728971962616, 'recall': 0.7424242424242424, 'f1': 0.6814159292035398, 'number': 726} | 0.6756 | 0.7512 | 0.7114 | 0.7180 |
| 0.0376 | 21.0526 | 400 | 1.5753 | {'precision': 0.7734447539461468, 'recall': 0.7677419354838709, 'f1': 0.7705827937095282, 'number': 1085} | {'precision': 0.5, 'recall': 0.39655172413793105, 'f1': 0.4423076923076923, 'number': 58} | {'precision': 0.6336302895322939, 'recall': 0.7837465564738292, 'f1': 0.7007389162561576, 'number': 726} | 0.7051 | 0.7624 | 0.7326 | 0.7219 |
| 0.0093 | 31.5789 | 600 | 1.7096 | {'precision': 0.8057971014492754, 'recall': 0.7686635944700461, 'f1': 0.7867924528301887, 'number': 1085} | {'precision': 0.4807692307692308, 'recall': 0.43103448275862066, 'f1': 0.45454545454545453, 'number': 58} | {'precision': 0.643702906350915, 'recall': 0.8236914600550964, 'f1': 0.7226586102719034, 'number': 726} | 0.7227 | 0.7796 | 0.7501 | 0.7347 |
| 0.0063 | 42.1053 | 800 | 2.0163 | {'precision': 0.760705289672544, 'recall': 0.8350230414746543, 'f1': 0.7961335676625658, 'number': 1085} | {'precision': 0.55, 'recall': 0.3793103448275862, 'f1': 0.4489795918367347, 'number': 58} | {'precision': 0.698090692124105, 'recall': 0.8057851239669421, 'f1': 0.748081841432225, 'number': 726} | 0.7313 | 0.8095 | 0.7684 | 0.7047 |
| 0.0041 | 52.6316 | 1000 | 1.7820 | {'precision': 0.773403324584427, 'recall': 0.8147465437788018, 'f1': 0.793536804308797, 'number': 1085} | {'precision': 0.627906976744186, 'recall': 0.46551724137931033, 'f1': 0.5346534653465347, 'number': 58} | {'precision': 0.6758544652701213, 'recall': 0.8443526170798898, 'f1': 0.7507654623392529, 'number': 726} | 0.7281 | 0.8154 | 0.7693 | 0.7344 |
| 0.0023 | 63.1579 | 1200 | 1.9631 | {'precision': 0.8218390804597702, 'recall': 0.7907834101382488, 'f1': 0.8060122123062471, 'number': 1085} | {'precision': 0.6444444444444445, 'recall': 0.5, 'f1': 0.5631067961165049, 'number': 58} | {'precision': 0.6841491841491841, 'recall': 0.8085399449035813, 'f1': 0.7411616161616162, 'number': 726} | 0.7571 | 0.7887 | 0.7725 | 0.7379 |
| 0.0016 | 73.6842 | 1400 | 1.8911 | {'precision': 0.7939282428702852, 'recall': 0.7953917050691244, 'f1': 0.794659300184162, 'number': 1085} | {'precision': 0.5909090909090909, 'recall': 0.4482758620689655, 'f1': 0.5098039215686274, 'number': 58} | {'precision': 0.6629834254143646, 'recall': 0.8264462809917356, 'f1': 0.7357449417535254, 'number': 726} | 0.7313 | 0.7967 | 0.7626 | 0.7442 |
| 0.0012 | 84.2105 | 1600 | 1.9599 | {'precision': 0.8111954459203036, 'recall': 0.7880184331797235, 'f1': 0.7994389901823281, 'number': 1085} | {'precision': 0.6666666666666666, 'recall': 0.41379310344827586, 'f1': 0.5106382978723404, 'number': 58} | {'precision': 0.7020785219399538, 'recall': 0.837465564738292, 'f1': 0.7638190954773869, 'number': 726} | 0.7602 | 0.7956 | 0.7775 | 0.7356 |
| 0.0006 | 94.7368 | 1800 | 2.1117 | {'precision': 0.8485772357723578, 'recall': 0.7695852534562212, 'f1': 0.8071532141130981, 'number': 1085} | {'precision': 0.6444444444444445, 'recall': 0.5, 'f1': 0.5631067961165049, 'number': 58} | {'precision': 0.7149817295980512, 'recall': 0.8085399449035813, 'f1': 0.7588881706528765, 'number': 726} | 0.7843 | 0.7764 | 0.7803 | 0.7377 |
| 0.0006 | 105.2632 | 2000 | 2.0033 | {'precision': 0.8036866359447005, 'recall': 0.8036866359447005, 'f1': 0.8036866359447006, 'number': 1085} | {'precision': 0.6410256410256411, 'recall': 0.43103448275862066, 'f1': 0.5154639175257731, 'number': 58} | {'precision': 0.6780973451327433, 'recall': 0.8443526170798898, 'f1': 0.7521472392638038, 'number': 726} | 0.7446 | 0.8079 | 0.7750 | 0.7409 |
| 0.0003 | 115.7895 | 2200 | 2.0170 | {'precision': 0.8059836808703535, 'recall': 0.8193548387096774, 'f1': 0.8126142595978061, 'number': 1085} | {'precision': 0.6590909090909091, 'recall': 0.5, 'f1': 0.5686274509803921, 'number': 58} | {'precision': 0.7037037037037037, 'recall': 0.837465564738292, 'f1': 0.7647798742138364, 'number': 726} | 0.7588 | 0.8165 | 0.7866 | 0.7433 |
| 0.0003 | 126.3158 | 2400 | 2.0173 | {'precision': 0.8104761904761905, 'recall': 0.784331797235023, 'f1': 0.7971896955503512, 'number': 1085} | {'precision': 0.6363636363636364, 'recall': 0.4827586206896552, 'f1': 0.5490196078431373, 'number': 58} | {'precision': 0.7096018735362998, 'recall': 0.8347107438016529, 'f1': 0.7670886075949367, 'number': 726} | 0.7623 | 0.7945 | 0.7781 | 0.7393 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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