File size: 4,430 Bytes
fd81e68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | ---
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
|