|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: rubert-base-cased-sentence-finetuned-sent_in_ru |
|
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. --> |
|
|
|
# rubert-base-cased-sentence-finetuned-sent_in_ru |
|
|
|
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.3503 |
|
- Accuracy: 0.6884 |
|
- F1: 0.6875 |
|
|
|
## 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: 15 |
|
- eval_batch_size: 15 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 25 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
|
| No log | 1.0 | 441 | 0.7397 | 0.6630 | 0.6530 | |
|
| 0.771 | 2.0 | 882 | 0.7143 | 0.6909 | 0.6905 | |
|
| 0.5449 | 3.0 | 1323 | 0.8385 | 0.6897 | 0.6870 | |
|
| 0.3795 | 4.0 | 1764 | 0.8851 | 0.6939 | 0.6914 | |
|
| 0.3059 | 5.0 | 2205 | 1.0728 | 0.6933 | 0.6953 | |
|
| 0.2673 | 6.0 | 2646 | 1.0673 | 0.7060 | 0.7020 | |
|
| 0.2358 | 7.0 | 3087 | 1.5200 | 0.6830 | 0.6829 | |
|
| 0.2069 | 8.0 | 3528 | 1.3439 | 0.7024 | 0.7016 | |
|
| 0.2069 | 9.0 | 3969 | 1.3545 | 0.6830 | 0.6833 | |
|
| 0.1724 | 10.0 | 4410 | 1.5591 | 0.6927 | 0.6902 | |
|
| 0.1525 | 11.0 | 4851 | 1.6425 | 0.6818 | 0.6823 | |
|
| 0.131 | 12.0 | 5292 | 1.8999 | 0.6836 | 0.6775 | |
|
| 0.1253 | 13.0 | 5733 | 1.6959 | 0.6884 | 0.6877 | |
|
| 0.1132 | 14.0 | 6174 | 1.9561 | 0.6776 | 0.6803 | |
|
| 0.0951 | 15.0 | 6615 | 2.0356 | 0.6763 | 0.6754 | |
|
| 0.1009 | 16.0 | 7056 | 1.7995 | 0.6842 | 0.6741 | |
|
| 0.1009 | 17.0 | 7497 | 2.0638 | 0.6884 | 0.6811 | |
|
| 0.0817 | 18.0 | 7938 | 2.1686 | 0.6884 | 0.6859 | |
|
| 0.0691 | 19.0 | 8379 | 2.0874 | 0.6878 | 0.6889 | |
|
| 0.0656 | 20.0 | 8820 | 2.1772 | 0.6854 | 0.6817 | |
|
| 0.0652 | 21.0 | 9261 | 2.4018 | 0.6872 | 0.6896 | |
|
| 0.0608 | 22.0 | 9702 | 2.2074 | 0.6770 | 0.6656 | |
|
| 0.0677 | 23.0 | 10143 | 2.2101 | 0.6848 | 0.6793 | |
|
| 0.0559 | 24.0 | 10584 | 2.2920 | 0.6848 | 0.6835 | |
|
| 0.0524 | 25.0 | 11025 | 2.3503 | 0.6884 | 0.6875 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.11.2 |
|
- Pytorch 1.9.0+cu102 |
|
- Datasets 1.12.1 |
|
- Tokenizers 0.10.3 |
|
|
