metadata
language:
- ru
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: >-
Однажды я посетил прекрасный городок в горах. На его улицах росли
удивительные цветы.
example_title: Example_1
pipeline_tag: token-classification
base_model: DeepPavlov/rubert-base-cased
model-index:
- name: rubert-base-cased-token
results: []
rubert-base-cased-token
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the OpenCorpora dataset opencorpora.org. It achieves the following results on the evaluation set:
- Loss: 0.2595
- Precision: 0.9304
- Recall: 0.9334
- F1: 0.9319
- Accuracy: 0.9424
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Tokens classification from OpenCorpora: opencorpora.org
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
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 69 | 0.6926 | 0.7731 | 0.7674 | 0.7702 | 0.8200 |
| No log | 2.0 | 138 | 0.3744 | 0.8665 | 0.8807 | 0.8735 | 0.9003 |
| No log | 3.0 | 207 | 0.2891 | 0.9004 | 0.9071 | 0.9037 | 0.9231 |
| No log | 4.0 | 276 | 0.2566 | 0.9123 | 0.9217 | 0.9170 | 0.9327 |
| No log | 5.0 | 345 | 0.2587 | 0.9211 | 0.9255 | 0.9233 | 0.9366 |
| No log | 6.0 | 414 | 0.2472 | 0.9264 | 0.9289 | 0.9276 | 0.9401 |
| No log | 7.0 | 483 | 0.2589 | 0.9267 | 0.9313 | 0.9290 | 0.9406 |
| 0.3825 | 8.0 | 552 | 0.2559 | 0.9286 | 0.9334 | 0.9310 | 0.9416 |
| 0.3825 | 9.0 | 621 | 0.2578 | 0.9304 | 0.9339 | 0.9321 | 0.9425 |
| 0.3825 | 10.0 | 690 | 0.2595 | 0.9304 | 0.9334 | 0.9319 | 0.9424 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2