| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: rubert-tiny2-srl |
| 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-tiny2-srl |
|
|
| This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2041 |
| - Addressee Precision: 0.7273 |
| - Addressee Recall: 0.8 |
| - Addressee F1: 0.7619 |
| - Addressee Number: 10 |
| - Benefactive Precision: 0.0 |
| - Benefactive Recall: 0.0 |
| - Benefactive F1: 0.0 |
| - Benefactive Number: 1 |
| - Causator Precision: 0.8824 |
| - Causator Recall: 0.8333 |
| - Causator F1: 0.8571 |
| - Causator Number: 18 |
| - Cause Precision: 0.6667 |
| - Cause Recall: 0.1538 |
| - Cause F1: 0.25 |
| - Cause Number: 13 |
| - Contrsubject Precision: 0.6667 |
| - Contrsubject Recall: 0.3333 |
| - Contrsubject F1: 0.4444 |
| - Contrsubject Number: 6 |
| - Deliberative Precision: 1.0 |
| - Deliberative Recall: 0.4 |
| - Deliberative F1: 0.5714 |
| - Deliberative Number: 5 |
| - Experiencer Precision: 0.7660 |
| - Experiencer Recall: 0.8 |
| - Experiencer F1: 0.7826 |
| - Experiencer Number: 90 |
| - Object Precision: 0.7576 |
| - Object Recall: 0.6868 |
| - Object F1: 0.7205 |
| - Object Number: 182 |
| - Predicate Precision: 0.9713 |
| - Predicate Recall: 0.9967 |
| - Predicate F1: 0.9839 |
| - Predicate Number: 306 |
| - Overall Precision: 0.8719 |
| - Overall Recall: 0.8415 |
| - Overall F1: 0.8565 |
| - Overall Accuracy: 0.9429 |
|
|
| ## 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: 0.00018632464179881193 |
| - train_batch_size: 4 |
| - eval_batch_size: 1 |
| - seed: 755657 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.02 |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Addressee Precision | Addressee Recall | Addressee F1 | Addressee Number | Benefactive Precision | Benefactive Recall | Benefactive F1 | Benefactive Number | Causator Precision | Causator Recall | Causator F1 | Causator Number | Cause Precision | Cause Recall | Cause F1 | Cause Number | Contrsubject Precision | Contrsubject Recall | Contrsubject F1 | Contrsubject Number | Deliberative Precision | Deliberative Recall | Deliberative F1 | Deliberative Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Object Precision | Object Recall | Object F1 | Object Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
| | 0.2845 | 1.0 | 181 | 0.2356 | 0.8 | 0.8 | 0.8000 | 10 | 0.0 | 0.0 | 0.0 | 1 | 0.7895 | 0.8333 | 0.8108 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.0 | 0.0 | 0.0 | 6 | 0.0 | 0.0 | 0.0 | 5 | 0.7320 | 0.7889 | 0.7594 | 90 | 0.7740 | 0.6209 | 0.6890 | 182 | 0.9744 | 0.9935 | 0.9838 | 306 | 0.875 | 0.8098 | 0.8412 | 0.9376 | |
| | 0.1875 | 1.99 | 362 | 0.2041 | 0.7273 | 0.8 | 0.7619 | 10 | 0.0 | 0.0 | 0.0 | 1 | 0.8824 | 0.8333 | 0.8571 | 18 | 0.6667 | 0.1538 | 0.25 | 13 | 0.6667 | 0.3333 | 0.4444 | 6 | 1.0 | 0.4 | 0.5714 | 5 | 0.7660 | 0.8 | 0.7826 | 90 | 0.7576 | 0.6868 | 0.7205 | 182 | 0.9713 | 0.9967 | 0.9839 | 306 | 0.8719 | 0.8415 | 0.8565 | 0.9429 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.28.1 |
| - Pytorch 2.0.0+cu117 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
|
|
