--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: EstBERT128_Rubric results: - task: name: Text Classification type: text-classification metrics: - name: Accuracy type: accuracy value: 0.8329238295555115 --- # EstBERT128_Rubric This model is a fine-tuned version of [tartuNLP/EstBERT](https://huggingface.co/tartuNLP/EstBERT). It achieves the following results on the test set: - Loss: 2.0552 - Accuracy: 0.8329 ## Model description A single linear layer classifier is fit on top of the last layer [CLS] token representation. The model is fully fine-tuned during training. ## Intended uses & limitations This model is intended to be used as it is. It can be used to predict nine rubric categories of Estonian texts. We do not guarantee that the model is useful for anything or that the predictions are accurate on new data. ## Training and evaluation data The model was trained and evaluated on the rubric categories of the [Estonian Valence dataset](http://peeter.eki.ee:5000/valence/paragraphsquery). The data was split into train/dev/test parts with 70/10/20 proportions. The nine rubric labels in the Estonian Valence dataset are: - ARVAMUS (opinion) - EESTI (domestic) - ELU-O (life) - KOMM-O-ELU (comments) - KOMM-P-EESTI (comments) - KRIMI (crime) - KULTUUR (culture) - SPORT (sports) - VALISMAA (world) It probably makes sense to treat the two comments categories (KOMM-O-ELU and KOMM-P-EESTI) as a single category. ## Training procedure The model was trained for maximu 100 epochs using early stopping procedure. After every epoch, the accuracy was calculated on the development set. If the development set accuracy did not improve for 20 epochs, the training was stopped. ### 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 The final model was taken after 39th epoch. | 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