--- license: mit base_model: cointegrated/rubert-tiny2 tags: - generated_from_trainer model-index: - name: rubert-tiny2-srl results: [] --- # 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 ## 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: 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 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3