--- license: mit base_model: ai-forever/ruElectra-medium tags: - generated_from_trainer model-index: - name: rubert-electra-srl results: [] --- # rubert-electra-srl This model is a fine-tuned version of [ai-forever/ruElectra-medium](https://huggingface.co./ai-forever/ruElectra-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0564 - Addressee Precision: 0.8710 - Addressee Recall: 0.9153 - Addressee F1: 0.8926 - Addressee Number: 59 - Benefactive Precision: 0.0 - Benefactive Recall: 0.0 - Benefactive F1: 0.0 - Benefactive Number: 8 - Causator Precision: 0.9007 - Causator Recall: 0.9379 - Causator F1: 0.9189 - Causator Number: 145 - Cause Precision: 0.8491 - Cause Recall: 0.7895 - Cause F1: 0.8182 - Cause Number: 114 - Contrsubject Precision: 0.872 - Contrsubject Recall: 0.9008 - Contrsubject F1: 0.8862 - Contrsubject Number: 121 - Deliberative Precision: 0.7439 - Deliberative Recall: 0.9385 - Deliberative F1: 0.8299 - Deliberative Number: 65 - Destinative Precision: 1.0 - Destinative Recall: 0.5238 - Destinative F1: 0.6875 - Destinative Number: 21 - Directivefinal Precision: 1.0 - Directivefinal Recall: 0.7 - Directivefinal F1: 0.8235 - Directivefinal Number: 10 - Experiencer Precision: 0.9132 - Experiencer Recall: 0.9374 - Experiencer F1: 0.9252 - Experiencer Number: 1055 - Instrument Precision: 0.8409 - Instrument Recall: 0.7255 - Instrument F1: 0.7789 - Instrument Number: 51 - Limitative Precision: 0.0 - Limitative Recall: 0.0 - Limitative F1: 0.0 - Limitative Number: 3 - Object Precision: 0.9449 - Object Recall: 0.9389 - Object F1: 0.9419 - Object Number: 1898 - Overall Precision: 0.9210 - Overall Recall: 0.9228 - Overall F1: 0.9219 - Overall Accuracy: 0.9855 - Mediative Number: 0.0 - Mediative F1: 0.0 - Mediative Precision: 0.0 - Mediative Recall: 0.0 - Directiveinitial Number: 0.0 - Directiveinitial F1: 0.0 - Directiveinitial Precision: 0.0 - Directiveinitial Recall: 0.0 ## 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: 0.00016666401556632117 - train_batch_size: 1 - eval_batch_size: 1 - seed: 708526 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.21 - num_epochs: 3 - 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 | 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 | Mediative Number | Mediative F1 | Mediative Precision | Mediative Recall | Directiveinitial Number | Directiveinitial F1 | Directiveinitial Precision | Directiveinitial Recall | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:|:----------------:|:------------:|:-------------------:|:----------------:|:-----------------------:|:-------------------:|:--------------------------:|:-----------------------:| | 0.574 | 1.0 | 2942 | 0.5853 | 0.0 | 0.0 | 0.0 | 59 | 0.0 | 0.0 | 0.0 | 8 | 0.0 | 0.0 | 0.0 | 145 | 0.0 | 0.0 | 0.0 | 114 | 0.0 | 0.0 | 0.0 | 121 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 21 | 0.0 | 0.0 | 0.0 | 10 | 0.0 | 0.0 | 0.0 | 1055 | 0.0 | 0.0 | 0.0 | 51 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 1898 | 0.0 | 0.0 | 0.0 | 0.8893 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.1625 | 2.0 | 5884 | 0.1573 | 0.5714 | 0.8136 | 0.6713 | 59 | 0.0 | 0.0 | 0.0 | 8 | 0.6966 | 0.8552 | 0.7678 | 145 | 0.3186 | 0.6316 | 0.4235 | 114 | 0.6875 | 0.4545 | 0.5473 | 121 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 21 | 0.0 | 0.0 | 0.0 | 10 | 0.8504 | 0.8246 | 0.8373 | 1055 | 0.4769 | 0.6078 | 0.5345 | 51 | 0.0 | 0.0 | 0.0 | 3 | 0.8923 | 0.8161 | 0.8525 | 1898 | 0.8104 | 0.7744 | 0.7920 | 0.9634 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0838 | 3.0 | 8826 | 0.0564 | 0.8710 | 0.9153 | 0.8926 | 59 | 0.0 | 0.0 | 0.0 | 8 | 0.9007 | 0.9379 | 0.9189 | 145 | 0.8491 | 0.7895 | 0.8182 | 114 | 0.872 | 0.9008 | 0.8862 | 121 | 0.7439 | 0.9385 | 0.8299 | 65 | 1.0 | 0.5238 | 0.6875 | 21 | 1.0 | 0.7 | 0.8235 | 10 | 0.9132 | 0.9374 | 0.9252 | 1055 | 0.8409 | 0.7255 | 0.7789 | 51 | 0.0 | 0.0 | 0.0 | 3 | 0.9449 | 0.9389 | 0.9419 | 1898 | 0.9210 | 0.9228 | 0.9219 | 0.9855 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1