---
library_name: transformers
license: gemma
base_model: google/embeddinggemma-300m
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: universal_classifier
  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. -->

# universal_classifier

This model is a fine-tuned version of [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1127
- Accuracy: 0.685

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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: linear
- training_steps: 22500

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|
| 0.3060        | 0.0132 | 500   | 0.2985          | 0.175    |
| 0.2972        | 0.0264 | 1000  | 0.2958          | 0.192    |
| 0.2889        | 0.0396 | 1500  | 0.2951          | 0.193    |
| 0.2827        | 0.0527 | 2000  | 0.2945          | 0.216    |
| 0.2961        | 0.0659 | 2500  | 0.2883          | 0.208    |
| 0.2520        | 0.0791 | 3000  | 0.2509          | 0.359    |
| 0.2181        | 0.0923 | 3500  | 0.2166          | 0.449    |
| 0.1710        | 0.1055 | 4000  | 0.1697          | 0.561    |
| 0.1655        | 0.1187 | 4500  | 0.1563          | 0.588    |
| 0.1344        | 0.1319 | 5000  | 0.1433          | 0.607    |
| 0.1416        | 0.1451 | 5500  | 0.1378          | 0.631    |
| 0.1347        | 0.1582 | 6000  | 0.1369          | 0.609    |
| 0.1250        | 0.1714 | 6500  | 0.1358          | 0.63     |
| 0.1560        | 0.1846 | 7000  | 0.1332          | 0.638    |
| 0.1368        | 0.1978 | 7500  | 0.1322          | 0.648    |
| 0.1275        | 0.2110 | 8000  | 0.1331          | 0.649    |
| 0.1257        | 0.2242 | 8500  | 0.1297          | 0.654    |
| 0.1349        | 0.2374 | 9000  | 0.1288          | 0.657    |
| 0.1306        | 0.2506 | 9500  | 0.1262          | 0.655    |
| 0.1161        | 0.2637 | 10000 | 0.1243          | 0.652    |
| 0.1315        | 0.2769 | 10500 | 0.1249          | 0.666    |
| 0.1298        | 0.2901 | 11000 | 0.1245          | 0.659    |
| 0.1141        | 0.3033 | 11500 | 0.1221          | 0.664    |
| 0.1216        | 0.3165 | 12000 | 0.1205          | 0.668    |
| 0.1216        | 0.3297 | 12500 | 0.1204          | 0.67     |
| 0.1211        | 0.3429 | 13000 | 0.1214          | 0.671    |
| 0.1179        | 0.3561 | 13500 | 0.1204          | 0.666    |
| 0.1246        | 0.3692 | 14000 | 0.1176          | 0.67     |
| 0.1132        | 0.3824 | 14500 | 0.1170          | 0.669    |
| 0.1190        | 0.3956 | 15000 | 0.1177          | 0.672    |
| 0.1075        | 0.4088 | 15500 | 0.1173          | 0.688    |
| 0.1177        | 0.4220 | 16000 | 0.1140          | 0.683    |
| 0.0958        | 0.4352 | 16500 | 0.1150          | 0.678    |
| 0.1247        | 0.4484 | 17000 | 0.1147          | 0.676    |
| 0.1059        | 0.4615 | 17500 | 0.1138          | 0.687    |
| 0.1058        | 0.4747 | 18000 | 0.1144          | 0.681    |
| 0.1070        | 0.4879 | 18500 | 0.1146          | 0.69     |
| 0.1166        | 0.5011 | 19000 | 0.1134          | 0.691    |
| 0.1139        | 0.5143 | 19500 | 0.1128          | 0.684    |
| 0.1104        | 0.5275 | 20000 | 0.1139          | 0.685    |
| 0.1080        | 0.5407 | 20500 | 0.1144          | 0.677    |
| 0.1145        | 0.5539 | 21000 | 0.1128          | 0.698    |
| 0.1246        | 0.5670 | 21500 | 0.1126          | 0.688    |
| 0.1258        | 0.5802 | 22000 | 0.1128          | 0.679    |
| 0.1089        | 0.5934 | 22500 | 0.1127          | 0.685    |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2