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---
license: mit
tags:
- generated_from_trainer
base_model: deepset/gbert-large
model-index:
- name: gbert-large-finetuned-cust
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. -->
# gbert-large-finetuned-cust
This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1846
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8251 | 1.0 | 157 | 0.5204 |
| 0.508 | 2.0 | 314 | 0.3953 |
| 0.4009 | 3.0 | 471 | 0.3242 |
| 0.3587 | 4.0 | 628 | 0.3300 |
| 0.3276 | 5.0 | 785 | 0.3137 |
| 0.302 | 6.0 | 942 | 0.2826 |
| 0.2777 | 7.0 | 1099 | 0.2768 |
| 0.2609 | 8.0 | 1256 | 0.2726 |
| 0.244 | 9.0 | 1413 | 0.2660 |
| 0.2274 | 10.0 | 1570 | 0.2391 |
| 0.2132 | 11.0 | 1727 | 0.2353 |
| 0.2014 | 12.0 | 1884 | 0.2134 |
| 0.1835 | 13.0 | 2041 | 0.2278 |
| 0.1896 | 14.0 | 2198 | 0.2110 |
| 0.1974 | 15.0 | 2355 | 0.2132 |
| 0.1775 | 16.0 | 2512 | 0.1973 |
| 0.1715 | 17.0 | 2669 | 0.1941 |
| 0.1777 | 18.0 | 2826 | 0.2105 |
| 0.1741 | 19.0 | 2983 | 0.2127 |
| 0.1607 | 20.0 | 3140 | 0.1762 |
| 0.1562 | 21.0 | 3297 | 0.2095 |
| 0.1548 | 22.0 | 3454 | 0.1805 |
| 0.1534 | 23.0 | 3611 | 0.1852 |
| 0.1484 | 24.0 | 3768 | 0.1773 |
| 0.1473 | 25.0 | 3925 | 0.1759 |
| 0.1354 | 26.0 | 4082 | 0.1734 |
| 0.136 | 27.0 | 4239 | 0.1902 |
| 0.1306 | 28.0 | 4396 | 0.1769 |
| 0.1353 | 29.0 | 4553 | 0.1705 |
| 0.1368 | 30.0 | 4710 | 0.1846 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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