metadata
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 128Bert
results: []
128Bert
This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8346
- Accuracy: 0.7033
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-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1934 | 1.0 | 2074 | 1.1488 | 0.6027 |
| 1.0626 | 2.0 | 4148 | 1.0247 | 0.6459 |
| 0.9729 | 3.0 | 6222 | 0.9483 | 0.6658 |
| 0.908 | 4.0 | 8296 | 0.9041 | 0.6811 |
| 0.8684 | 5.0 | 10370 | 0.8771 | 0.6897 |
| 0.8348 | 6.0 | 12444 | 0.8593 | 0.6956 |
| 0.8055 | 7.0 | 14518 | 0.8507 | 0.6991 |
| 0.7924 | 8.0 | 16592 | 0.8410 | 0.7017 |
| 0.7857 | 9.0 | 18666 | 0.8349 | 0.7037 |
| 0.7732 | 10.0 | 20740 | 0.8346 | 0.7033 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1