| --- |
| license: mit |
| base_model: cointegrated/rubert-tiny2 |
| 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: 0.1428 |
| - Addressee Precision: 0.6364 |
| - Addressee Recall: 0.875 |
| - Addressee F1: 0.7368 |
| - Addressee Number: 8 |
| - Benefactive Precision: 0.0 |
| - Benefactive Recall: 0.0 |
| - Benefactive F1: 0.0 |
| - Benefactive Number: 2 |
| - Causator Precision: 0.9286 |
| - Causator Recall: 0.8125 |
| - Causator F1: 0.8667 |
| - Causator Number: 16 |
| - Cause Precision: 0.6 |
| - Cause Recall: 0.25 |
| - Cause F1: 0.3529 |
| - Cause Number: 12 |
| - Contrsubject Precision: 0.6364 |
| - Contrsubject Recall: 0.4118 |
| - Contrsubject F1: 0.5 |
| - Contrsubject Number: 17 |
| - Deliberative Precision: 1.0 |
| - Deliberative Recall: 0.6667 |
| - Deliberative F1: 0.8 |
| - Deliberative Number: 6 |
| - Destinative Precision: 1.0 |
| - Destinative Recall: 0.5 |
| - Destinative F1: 0.6667 |
| - Destinative Number: 4 |
| - Directivefinal Precision: 1.0 |
| - Directivefinal Recall: 1.0 |
| - Directivefinal F1: 1.0 |
| - Directivefinal Number: 2 |
| - Experiencer Precision: 0.8018 |
| - Experiencer Recall: 0.9368 |
| - Experiencer F1: 0.8641 |
| - Experiencer Number: 95 |
| - Instrument Precision: 0.0 |
| - Instrument Recall: 0.0 |
| - Instrument F1: 0.0 |
| - Instrument Number: 3 |
| - Limitative Precision: 0.0 |
| - Limitative Recall: 0.0 |
| - Limitative F1: 0.0 |
| - Limitative Number: 1 |
| - Object Precision: 0.7589 |
| - Object Recall: 0.8 |
| - Object F1: 0.7789 |
| - Object Number: 240 |
| - Overall Precision: 0.7724 |
| - Overall Recall: 0.7857 |
| - Overall F1: 0.7790 |
| - Overall Accuracy: 0.9589 |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
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|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 8.017672397578385e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 1 |
| - seed: 678943 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.04 |
| - num_epochs: 4 |
|
|
| ### 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 | Destinative Precision | Destinative Recall | Destinative F1 | Destinative Number | Directivefinal Precision | Directivefinal Recall | Directivefinal F1 | Directivefinal Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Instrument Precision | Instrument Recall | Instrument F1 | Instrument Number | Limitative Precision | Limitative Recall | Limitative F1 | Limitative Number | Object Precision | Object Recall | Object F1 | Object Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
| | 0.2206 | 1.0 | 490 | 0.1959 | 0.6667 | 0.25 | 0.3636 | 8 | 0.0 | 0.0 | 0.0 | 2 | 0.8667 | 0.8125 | 0.8387 | 16 | 0.0 | 0.0 | 0.0 | 12 | 1.0 | 0.0588 | 0.1111 | 17 | 0.0 | 0.0 | 0.0 | 6 | 0.0 | 0.0 | 0.0 | 4 | 0.0 | 0.0 | 0.0 | 2 | 0.7203 | 0.8947 | 0.7981 | 95 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.6692 | 0.725 | 0.696 | 240 | 0.6927 | 0.6773 | 0.6849 | 0.9445 | |
| | 0.1507 | 2.0 | 981 | 0.1492 | 0.5556 | 0.625 | 0.5882 | 8 | 0.0 | 0.0 | 0.0 | 2 | 0.8667 | 0.8125 | 0.8387 | 16 | 0.6 | 0.25 | 0.3529 | 12 | 0.75 | 0.3529 | 0.48 | 17 | 1.0 | 0.1667 | 0.2857 | 6 | 1.0 | 0.25 | 0.4 | 4 | 1.0 | 1.0 | 1.0 | 2 | 0.8646 | 0.8737 | 0.8691 | 95 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.7635 | 0.7667 | 0.7651 | 240 | 0.7884 | 0.7340 | 0.7602 | 0.9566 | |
| | 0.1146 | 3.0 | 1472 | 0.1437 | 0.6364 | 0.875 | 0.7368 | 8 | 0.0 | 0.0 | 0.0 | 2 | 0.9286 | 0.8125 | 0.8667 | 16 | 0.6 | 0.25 | 0.3529 | 12 | 0.6429 | 0.5294 | 0.5806 | 17 | 1.0 | 0.5 | 0.6667 | 6 | 1.0 | 0.5 | 0.6667 | 4 | 1.0 | 1.0 | 1.0 | 2 | 0.8 | 0.9263 | 0.8585 | 95 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.7443 | 0.8125 | 0.7769 | 240 | 0.7612 | 0.7931 | 0.7768 | 0.9584 | |
| | 0.0842 | 3.99 | 1960 | 0.1428 | 0.6364 | 0.875 | 0.7368 | 8 | 0.0 | 0.0 | 0.0 | 2 | 0.9286 | 0.8125 | 0.8667 | 16 | 0.6 | 0.25 | 0.3529 | 12 | 0.6364 | 0.4118 | 0.5 | 17 | 1.0 | 0.6667 | 0.8 | 6 | 1.0 | 0.5 | 0.6667 | 4 | 1.0 | 1.0 | 1.0 | 2 | 0.8018 | 0.9368 | 0.8641 | 95 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.7589 | 0.8 | 0.7789 | 240 | 0.7724 | 0.7857 | 0.7790 | 0.9589 | |
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| ### Framework versions |
|
|
| - Transformers 4.33.2 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.5 |
| - Tokenizers 0.13.3 |
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|
