---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: HybridTimeScaleModel
  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. -->

# HybridTimeScaleModel

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: 3.0226

## 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: 1e-06
- train_batch_size: 22
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- training_steps: 77000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 4.1200        | 0.0091 | 1000  | 4.3751          |
| 3.5707        | 0.0182 | 2000  | 3.8969          |
| 3.3944        | 0.0273 | 3000  | 3.7320          |
| 3.2534        | 0.0364 | 4000  | 3.6268          |
| 3.2178        | 0.0455 | 5000  | 3.5597          |
| 3.1700        | 0.0545 | 6000  | 3.4946          |
| 3.1400        | 0.0636 | 7000  | 3.4605          |
| 3.0755        | 0.0727 | 8000  | 3.4183          |
| 3.0527        | 0.0818 | 9000  | 3.3907          |
| 3.0213        | 0.0909 | 10000 | 3.3656          |
| 3.0590        | 0.1    | 11000 | 3.3479          |
| 2.9770        | 0.1091 | 12000 | 3.3242          |
| 2.9614        | 0.1182 | 13000 | 3.3102          |
| 2.9506        | 0.1273 | 14000 | 3.2945          |
| 2.9333        | 0.1364 | 15000 | 3.2715          |
| 2.8988        | 0.1455 | 16000 | 3.2674          |
| 2.9117        | 0.1545 | 17000 | 3.2496          |
| 2.8933        | 0.1636 | 18000 | 3.2416          |
| 2.8866        | 0.1727 | 19000 | 3.2224          |
| 2.9030        | 0.1818 | 20000 | 3.2188          |
| 2.9004        | 0.1909 | 21000 | 3.2108          |
| 2.8540        | 0.2    | 22000 | 3.1956          |
| 2.8207        | 0.2091 | 23000 | 3.1900          |
| 2.8272        | 0.2182 | 24000 | 3.1836          |
| 2.8192        | 0.2273 | 25000 | 3.1781          |
| 2.7933        | 0.2364 | 26000 | 3.1706          |
| 2.8126        | 0.2455 | 27000 | 3.1654          |
| 2.8480        | 0.2545 | 28000 | 3.1647          |
| 2.7938        | 0.2636 | 29000 | 3.1548          |
| 2.7823        | 0.2727 | 30000 | 3.1496          |
| 2.7479        | 0.2818 | 31000 | 3.1456          |
| 2.7778        | 0.2909 | 32000 | 3.1463          |
| 2.7698        | 0.0091 | 33000 | 3.1402          |
| 2.7731        | 0.0182 | 34000 | 3.1313          |
| 2.7693        | 0.0273 | 35000 | 3.1262          |
| 2.7321        | 0.0364 | 36000 | 3.1245          |
| 2.6874        | 0.0091 | 37000 | 3.1317          |
| 2.6970        | 0.0182 | 38000 | 3.1285          |
| 2.6958        | 0.0273 | 39000 | 3.1291          |
| 2.6649        | 0.0364 | 40000 | 3.1292          |
| 2.7598        | 0.0455 | 41000 | 3.1212          |
| 2.7124        | 0.0545 | 42000 | 3.1164          |
| 2.7572        | 0.0636 | 43000 | 3.1164          |
| 2.7247        | 0.0727 | 44000 | 3.1065          |
| 2.7101        | 0.0818 | 45000 | 3.1004          |
| 2.6942        | 0.0909 | 46000 | 3.0926          |
| 2.7299        | 0.1    | 47000 | 3.0938          |
| 2.7021        | 0.1091 | 48000 | 3.0897          |
| 2.6787        | 0.1182 | 49000 | 3.0872          |
| 2.6781        | 0.1273 | 50000 | 3.0828          |
| 2.7094        | 0.1364 | 51000 | 3.0797          |
| 2.7144        | 0.1455 | 52000 | 3.0822          |
| 2.6800        | 0.1545 | 53000 | 3.0733          |
| 2.6689        | 0.1636 | 54000 | 3.0703          |
| 2.6780        | 0.1727 | 55000 | 3.0706          |
| 2.6806        | 0.1818 | 56000 | 3.0680          |
| 2.7088        | 0.1909 | 57000 | 3.0626          |
| 2.6277        | 0.2    | 58000 | 3.0661          |
| 2.6540        | 0.2091 | 59000 | 3.0577          |
| 2.6756        | 0.2182 | 60000 | 3.0541          |
| 2.6778        | 0.2273 | 61000 | 3.0542          |
| 2.6535        | 0.2364 | 62000 | 3.0504          |
| 2.6557        | 0.2455 | 63000 | 3.0508          |
| 2.6445        | 0.2545 | 64000 | 3.0473          |
| 2.6488        | 0.2636 | 65000 | 3.0462          |
| 2.6457        | 0.2727 | 66000 | 3.0442          |
| 2.6479        | 0.2818 | 67000 | 3.0378          |
| 2.6303        | 0.2909 | 68000 | 3.0449          |
| 2.6089        | 0.3    | 69000 | 3.0342          |
| 2.5966        | 0.3091 | 70000 | 3.0347          |
| 2.6226        | 0.3182 | 71000 | 3.0347          |
| 2.6174        | 0.3273 | 72000 | 3.0310          |
| 2.6171        | 0.3364 | 73000 | 3.0339          |
| 2.6139        | 0.3455 | 74000 | 3.0277          |
| 2.5904        | 0.3545 | 75000 | 3.0259          |
| 2.5940        | 0.3636 | 76000 | 3.0242          |
| 2.5818        | 0.0130 | 77000 | 3.0226          |


### Framework versions

- Transformers 5.13.1
- Pytorch 2.8.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2