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---
base_model: google-bert/bert-large-uncased
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: bert-large-uncased-swag
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. -->
# bert-large-uncased-swag
This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4643
- Accuracy: 0.8295
## 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: 16
- eval_batch_size: 16
- 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 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 1.2132 | 0.1088 | 500 | 0.8717 | 0.6959 |
| 0.908 | 0.2175 | 1000 | 0.7149 | 0.7473 |
| 0.8353 | 0.3263 | 1500 | 0.6474 | 0.7575 |
| 0.8075 | 0.4351 | 2000 | 0.6142 | 0.7798 |
| 0.8011 | 0.5438 | 2500 | 0.5785 | 0.7867 |
| 0.7727 | 0.6526 | 3000 | 0.5643 | 0.7936 |
| 0.7647 | 0.7614 | 3500 | 0.5698 | 0.7956 |
| 0.7731 | 0.8701 | 4000 | 0.5453 | 0.8011 |
| 0.7489 | 0.9789 | 4500 | 0.5336 | 0.8052 |
| 0.7496 | 1.0877 | 5000 | 0.5431 | 0.8033 |
| 0.735 | 1.1964 | 5500 | 0.5231 | 0.8083 |
| 0.7194 | 1.3052 | 6000 | 0.5147 | 0.8096 |
| 0.7307 | 1.4140 | 6500 | 0.5102 | 0.8112 |
| 0.7355 | 1.5227 | 7000 | 0.5223 | 0.8133 |
| 0.7085 | 1.6315 | 7500 | 0.5054 | 0.8142 |
| 0.7206 | 1.7403 | 8000 | 0.5026 | 0.8157 |
| 0.7143 | 1.8490 | 8500 | 0.5126 | 0.8144 |
| 0.7045 | 1.9578 | 9000 | 0.5035 | 0.8162 |
| 0.6972 | 2.0666 | 9500 | 0.4948 | 0.8178 |
| 0.6885 | 2.1753 | 10000 | 0.4890 | 0.8202 |
| 0.7079 | 2.2841 | 10500 | 0.4910 | 0.8193 |
| 0.6874 | 2.3929 | 11000 | 0.4907 | 0.8222 |
| 0.6832 | 2.5016 | 11500 | 0.4875 | 0.8217 |
| 0.6807 | 2.6104 | 12000 | 0.4824 | 0.8224 |
| 0.6865 | 2.7192 | 12500 | 0.4877 | 0.8227 |
| 0.6863 | 2.8279 | 13000 | 0.4821 | 0.8232 |
| 0.6913 | 2.9367 | 13500 | 0.4914 | 0.8229 |
| 0.6996 | 3.0455 | 14000 | 0.4843 | 0.8241 |
| 0.687 | 3.1542 | 14500 | 0.4753 | 0.8250 |
| 0.6896 | 3.2630 | 15000 | 0.4762 | 0.8251 |
| 0.6745 | 3.3718 | 15500 | 0.4753 | 0.8242 |
| 0.6735 | 3.4805 | 16000 | 0.4713 | 0.8267 |
| 0.6764 | 3.5893 | 16500 | 0.4715 | 0.8259 |
| 0.6521 | 3.6981 | 17000 | 0.4669 | 0.8285 |
| 0.6686 | 3.8068 | 17500 | 0.4726 | 0.8269 |
| 0.6721 | 3.9156 | 18000 | 0.4703 | 0.8273 |
| 0.6682 | 4.0244 | 18500 | 0.4660 | 0.8274 |
| 0.6533 | 4.1331 | 19000 | 0.4690 | 0.8281 |
| 0.6547 | 4.2419 | 19500 | 0.4697 | 0.8282 |
| 0.6589 | 4.3507 | 20000 | 0.4640 | 0.8291 |
| 0.6518 | 4.4594 | 20500 | 0.4638 | 0.8294 |
| 0.6739 | 4.5682 | 21000 | 0.4669 | 0.8285 |
| 0.6763 | 4.6770 | 21500 | 0.4628 | 0.8304 |
| 0.6503 | 4.7857 | 22000 | 0.4640 | 0.8296 |
| 0.6659 | 4.8945 | 22500 | 0.4643 | 0.8295 |
### Framework versions
- PEFT 0.12.1.dev0
- Transformers 4.45.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |