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---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: train-bioR-concat
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. -->
# train-bioR-concat
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6559
## 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: 0.001
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 96
- total_eval_batch_size: 96
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- training_steps: 41803
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.2076 | 0.0239 | 1000 | 1.8223 |
| 1.2901 | 0.0478 | 2000 | 1.8245 |
| 1.1528 | 0.0718 | 3000 | 1.8674 |
| 1.0056 | 0.0957 | 4000 | 1.9692 |
| 0.8399 | 0.1196 | 5000 | 2.0165 |
| 0.7892 | 0.1435 | 6000 | 1.9441 |
| 0.7658 | 0.1674 | 7000 | 1.8904 |
| 0.7284 | 0.1914 | 8000 | 1.8260 |
| 0.7217 | 0.2153 | 9000 | 1.8162 |
| 0.7122 | 0.2392 | 10000 | 1.7559 |
| 0.7055 | 0.2631 | 11000 | 1.7974 |
| 0.6943 | 0.2871 | 12000 | 1.7621 |
| 0.6942 | 0.3110 | 13000 | 1.7651 |
| 0.6868 | 0.3349 | 14000 | 1.7228 |
| 0.6817 | 0.3588 | 15000 | 1.7558 |
| 0.6911 | 0.3827 | 16000 | 1.7466 |
| 0.6889 | 0.4067 | 17000 | 1.7291 |
| 0.6798 | 0.4306 | 18000 | 1.6921 |
| 0.675 | 0.4545 | 19000 | 1.7139 |
| 0.6779 | 0.4784 | 20000 | 1.6933 |
| 0.6851 | 0.5023 | 21000 | 1.7136 |
| 0.675 | 0.5263 | 22000 | 1.6874 |
| 0.6747 | 0.5502 | 23000 | 1.6950 |
| 0.6724 | 0.5741 | 24000 | 1.6884 |
| 0.6631 | 0.5980 | 25000 | 1.6873 |
| 0.6671 | 0.6220 | 26000 | 1.6983 |
| 0.6645 | 0.6459 | 27000 | 1.6729 |
| 0.658 | 0.6698 | 28000 | 1.6809 |
| 0.6605 | 0.6937 | 29000 | 1.6656 |
| 0.6599 | 0.7176 | 30000 | 1.6704 |
| 0.6591 | 0.7416 | 31000 | 1.6679 |
| 0.6664 | 0.7655 | 32000 | 1.6555 |
| 0.6608 | 0.7894 | 33000 | 1.6487 |
| 0.6609 | 0.8133 | 34000 | 1.6522 |
| 0.6553 | 0.8372 | 35000 | 1.6502 |
| 0.6527 | 0.8612 | 36000 | 1.6568 |
| 0.6648 | 0.8851 | 37000 | 1.6587 |
| 0.6515 | 0.9090 | 38000 | 1.6471 |
| 0.65 | 0.9329 | 39000 | 1.6461 |
| 0.65 | 0.9568 | 40000 | 1.6499 |
| 0.6533 | 0.9808 | 41000 | 1.6559 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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