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---
base_model: google-bert/bert-base-uncased
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on [SWAG](https://huggingface.co./datasets/allenai/swag) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6749
- Accuracy: 0.7503
## Model description
More information needed
## Intended uses & limitations
This model should be used as an expert in the [Meteor-of-LoRA framework](https://github.com/ParagonLight/meteor-of-lora).
## Training and evaluation data
The data were splitted based on HuggingFace default dataset:
```python3
dataset = load_dataset("swag")
```
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 1.3807 | 0.1088 | 500 | 1.2507 | 0.6138 |
| 1.1949 | 0.2175 | 1000 | 1.0938 | 0.5737 |
| 1.132 | 0.3263 | 1500 | 1.0330 | 0.5657 |
| 1.0348 | 0.4351 | 2000 | 0.9162 | 0.6440 |
| 1.0008 | 0.5438 | 2500 | 0.8464 | 0.6801 |
| 0.9609 | 0.6526 | 3000 | 0.8267 | 0.6859 |
| 0.9454 | 0.7614 | 3500 | 0.8116 | 0.6943 |
| 0.9512 | 0.8701 | 4000 | 0.8125 | 0.6955 |
| 0.9367 | 0.9789 | 4500 | 0.7838 | 0.7032 |
| 0.9205 | 1.0877 | 5000 | 0.7861 | 0.7044 |
| 0.9189 | 1.1964 | 5500 | 0.7713 | 0.7088 |
| 0.8975 | 1.3052 | 6000 | 0.7538 | 0.7173 |
| 0.9065 | 1.4140 | 6500 | 0.7520 | 0.7175 |
| 0.8957 | 1.5227 | 7000 | 0.7513 | 0.7200 |
| 0.8768 | 1.6315 | 7500 | 0.7411 | 0.7195 |
| 0.8858 | 1.7403 | 8000 | 0.7306 | 0.7262 |
| 0.875 | 1.8490 | 8500 | 0.7302 | 0.7268 |
| 0.8649 | 1.9578 | 9000 | 0.7229 | 0.7303 |
| 0.8653 | 2.0666 | 9500 | 0.7126 | 0.7322 |
| 0.867 | 2.1753 | 10000 | 0.7198 | 0.7293 |
| 0.868 | 2.2841 | 10500 | 0.7125 | 0.7346 |
| 0.855 | 2.3929 | 11000 | 0.7051 | 0.7350 |
| 0.8557 | 2.5016 | 11500 | 0.7008 | 0.7384 |
| 0.8622 | 2.6104 | 12000 | 0.6979 | 0.7389 |
| 0.8506 | 2.7192 | 12500 | 0.7068 | 0.7378 |
| 0.8558 | 2.8279 | 13000 | 0.7082 | 0.7337 |
| 0.849 | 2.9367 | 13500 | 0.6978 | 0.7407 |
| 0.8581 | 3.0455 | 14000 | 0.6850 | 0.7460 |
| 0.8521 | 3.1542 | 14500 | 0.6945 | 0.7428 |
| 0.8454 | 3.2630 | 15000 | 0.6863 | 0.7446 |
| 0.8257 | 3.3718 | 15500 | 0.6917 | 0.7414 |
| 0.8522 | 3.4805 | 16000 | 0.6882 | 0.7445 |
| 0.8359 | 3.5893 | 16500 | 0.6845 | 0.7442 |
| 0.8238 | 3.6981 | 17000 | 0.6863 | 0.7441 |
| 0.8382 | 3.8068 | 17500 | 0.6937 | 0.7438 |
| 0.8326 | 3.9156 | 18000 | 0.6780 | 0.7488 |
| 0.8344 | 4.0244 | 18500 | 0.6775 | 0.7484 |
| 0.8224 | 4.1331 | 19000 | 0.6811 | 0.7477 |
| 0.8261 | 4.2419 | 19500 | 0.6797 | 0.7480 |
| 0.8256 | 4.3507 | 20000 | 0.6815 | 0.7481 |
| 0.8191 | 4.4594 | 20500 | 0.6788 | 0.7476 |
| 0.838 | 4.5682 | 21000 | 0.6802 | 0.7490 |
| 0.8383 | 4.6770 | 21500 | 0.6753 | 0.7498 |
| 0.8343 | 4.7857 | 22000 | 0.6762 | 0.7498 |
| 0.8381 | 4.8945 | 22500 | 0.6749 | 0.7503 |
### 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 |