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---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: midi_model_3
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. -->
# midi_model_3
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5542
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 2
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8047 | 0.33 | 300 | 0.7969 |
| 0.7924 | 0.66 | 600 | 0.7735 |
| 0.7758 | 1.0 | 900 | 0.7528 |
| 0.75 | 1.33 | 1200 | 0.7436 |
| 0.7432 | 1.66 | 1500 | 0.7277 |
| 0.7361 | 1.99 | 1800 | 0.7175 |
| 0.7121 | 2.32 | 2100 | 0.7025 |
| 0.708 | 2.65 | 2400 | 0.6861 |
| 0.6971 | 2.99 | 2700 | 0.6781 |
| 0.6777 | 3.32 | 3000 | 0.6718 |
| 0.6733 | 3.65 | 3300 | 0.6578 |
| 0.6643 | 3.98 | 3600 | 0.6500 |
| 0.6422 | 4.31 | 3900 | 0.6423 |
| 0.6401 | 4.65 | 4200 | 0.6330 |
| 0.6302 | 4.98 | 4500 | 0.6228 |
| 0.6103 | 5.31 | 4800 | 0.6148 |
| 0.6066 | 5.64 | 5100 | 0.6069 |
| 0.5995 | 5.97 | 5400 | 0.5979 |
| 0.5724 | 6.31 | 5700 | 0.5915 |
| 0.5772 | 6.64 | 6000 | 0.5870 |
| 0.5677 | 6.97 | 6300 | 0.5771 |
| 0.5491 | 7.3 | 6600 | 0.5740 |
| 0.5433 | 7.63 | 6900 | 0.5675 |
| 0.5384 | 7.96 | 7200 | 0.5630 |
| 0.5245 | 8.3 | 7500 | 0.5611 |
| 0.5206 | 8.63 | 7800 | 0.5578 |
| 0.5198 | 8.96 | 8100 | 0.5553 |
| 0.5141 | 9.29 | 8400 | 0.5544 |
| 0.5091 | 9.62 | 8700 | 0.5543 |
| 0.5096 | 9.96 | 9000 | 0.5542 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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