metadata
tags:
- generated_from_trainer
datasets:
- rubric
metrics:
- accuracy
model-index:
- name: EstBERT128_Rubric
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rubric
type: rubric
args: rubric
metrics:
- name: Accuracy
type: accuracy
value: 0.8329238295555115
EstBERT128_Rubric
This model is a fine-tuned version of tartuNLP/EstBERT on the rubric categories of the Estonian Valence dataset. It achieves the following results on the evaluation set:
- Loss: 2.0552
- Accuracy: 0.8329
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 3
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: polynomial
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1147 | 1.0 | 179 | 0.7421 | 0.7445 |
| 0.4323 | 2.0 | 358 | 0.6863 | 0.7813 |
| 0.1442 | 3.0 | 537 | 0.8545 | 0.7838 |
| 0.0496 | 4.0 | 716 | 1.2872 | 0.7494 |
| 0.0276 | 5.0 | 895 | 1.4702 | 0.7641 |
| 0.0202 | 6.0 | 1074 | 1.3764 | 0.7838 |
| 0.0144 | 7.0 | 1253 | 1.5762 | 0.7887 |
| 0.0078 | 8.0 | 1432 | 1.8806 | 0.7666 |
| 0.0177 | 9.0 | 1611 | 1.6159 | 0.7912 |
| 0.0223 | 10.0 | 1790 | 1.5863 | 0.7936 |
| 0.0108 | 11.0 | 1969 | 1.8051 | 0.7912 |
| 0.0201 | 12.0 | 2148 | 1.9344 | 0.7789 |
| 0.0252 | 13.0 | 2327 | 1.7978 | 0.8084 |
| 0.0104 | 14.0 | 2506 | 1.8779 | 0.7887 |
| 0.0138 | 15.0 | 2685 | 1.6456 | 0.8133 |
| 0.0066 | 16.0 | 2864 | 1.9668 | 0.7912 |
| 0.0148 | 17.0 | 3043 | 2.0068 | 0.7813 |
| 0.0128 | 18.0 | 3222 | 2.1539 | 0.7617 |
| 0.0115 | 19.0 | 3401 | 2.2490 | 0.7838 |
| 0.0186 | 20.0 | 3580 | 2.1768 | 0.7666 |
| 0.0051 | 21.0 | 3759 | 1.8859 | 0.7912 |
| 0.001 | 22.0 | 3938 | 2.0132 | 0.7912 |
| 0.0133 | 23.0 | 4117 | 1.8786 | 0.8084 |
| 0.0149 | 24.0 | 4296 | 2.2307 | 0.7961 |
| 0.014 | 25.0 | 4475 | 2.0041 | 0.8206 |
| 0.0132 | 26.0 | 4654 | 1.8872 | 0.8133 |
| 0.0079 | 27.0 | 4833 | 1.9357 | 0.7961 |
| 0.0078 | 28.0 | 5012 | 2.1891 | 0.7936 |
| 0.0126 | 29.0 | 5191 | 2.0207 | 0.8034 |
| 0.0003 | 30.0 | 5370 | 2.1917 | 0.8010 |
| 0.0015 | 31.0 | 5549 | 2.0417 | 0.8157 |
| 0.0056 | 32.0 | 5728 | 2.1172 | 0.8084 |
| 0.0058 | 33.0 | 5907 | 2.1921 | 0.8206 |
| 0.0001 | 34.0 | 6086 | 2.0079 | 0.8206 |
| 0.0031 | 35.0 | 6265 | 2.2447 | 0.8206 |
| 0.0007 | 36.0 | 6444 | 2.1802 | 0.8084 |
| 0.0061 | 37.0 | 6623 | 2.1103 | 0.8157 |
| 0.0 | 38.0 | 6802 | 2.2265 | 0.8084 |
| 0.0035 | 39.0 | 6981 | 2.0549 | 0.8329 |
| 0.0038 | 40.0 | 7160 | 2.1352 | 0.8182 |
| 0.0001 | 41.0 | 7339 | 2.0975 | 0.8108 |
| 0.0 | 42.0 | 7518 | 2.0833 | 0.8256 |
| 0.0 | 43.0 | 7697 | 2.1020 | 0.8280 |
| 0.0 | 44.0 | 7876 | 2.0841 | 0.8305 |
| 0.0 | 45.0 | 8055 | 2.2085 | 0.8182 |
| 0.0 | 46.0 | 8234 | 2.0756 | 0.8329 |
| 0.0 | 47.0 | 8413 | 2.1237 | 0.8305 |
| 0.0 | 48.0 | 8592 | 2.1217 | 0.8280 |
| 0.0052 | 49.0 | 8771 | 2.3567 | 0.8059 |
| 0.0014 | 50.0 | 8950 | 2.1710 | 0.8206 |
| 0.0032 | 51.0 | 9129 | 2.1452 | 0.8206 |
| 0.0 | 52.0 | 9308 | 2.2820 | 0.8133 |
| 0.0001 | 53.0 | 9487 | 2.2279 | 0.8157 |
| 0.0 | 54.0 | 9666 | 2.1841 | 0.8182 |
| 0.0 | 55.0 | 9845 | 2.1208 | 0.8231 |
| 0.0 | 56.0 | 10024 | 2.0967 | 0.8256 |
| 0.0002 | 57.0 | 10203 | 2.1911 | 0.8231 |
| 0.0 | 58.0 | 10382 | 2.2014 | 0.8231 |
| 0.0 | 59.0 | 10561 | 2.2014 | 0.8182 |
Framework versions
- Transformers 4.14.1
- Pytorch 1.10.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3