--- 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: 0.0448 - Addressee Precision: 0.9583 - Addressee Recall: 1.0 - Addressee F1: 0.9787 - Addressee Number: 23 - Benefactive Precision: 0.0 - Benefactive Recall: 0.0 - Benefactive F1: 0.0 - Benefactive Number: 2 - Causator Precision: 0.9773 - Causator Recall: 0.9773 - Causator F1: 0.9773 - Causator Number: 44 - Cause Precision: 0.9259 - Cause Recall: 0.7143 - Cause F1: 0.8065 - Cause Number: 35 - Contrsubject Precision: 1.0 - Contrsubject Recall: 0.9429 - Contrsubject F1: 0.9706 - Contrsubject Number: 35 - Deliberative Precision: 0.9231 - Deliberative Recall: 1.0 - Deliberative F1: 0.9600 - Deliberative Number: 24 - Destinative Precision: 1.0 - Destinative Recall: 1.0 - Destinative F1: 1.0 - Destinative Number: 7 - Directivefinal Precision: 1.0 - Directivefinal Recall: 1.0 - Directivefinal F1: 1.0 - Directivefinal Number: 1 - Experiencer Precision: 0.9030 - Experiencer Recall: 0.9441 - Experiencer F1: 0.9231 - Experiencer Number: 286 - Instrument Precision: 0.9 - Instrument Recall: 0.9 - Instrument F1: 0.9 - Instrument Number: 10 - Object Precision: 0.9484 - Object Recall: 0.9519 - Object F1: 0.9502 - Object Number: 541 - Overall Precision: 0.9369 - Overall Recall: 0.9425 - Overall F1: 0.9397 - Overall Accuracy: 0.9883 - Limitative F1: 0.0 - Limitative Number: 0.0 - Limitative Precision: 0.0 - Limitative Recall: 0.0 - Directiveinitial F1: 0.0 - Directiveinitial Number: 0.0 - Directiveinitial Precision: 0.0 - Directiveinitial Recall: 0.0 - Mediative F1: 0.0 - Mediative Number: 0.0 - Mediative Precision: 0.0 - Mediative 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: 8 - total_train_batch_size: 8 - 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 | Object Precision | Object Recall | Object F1 | Object Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Limitative F1 | Limitative Number | Limitative Precision | Limitative Recall | Directiveinitial F1 | Directiveinitial Number | Directiveinitial Precision | Directiveinitial Recall | Mediative F1 | Mediative Number | Mediative Precision | Mediative Recall | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------:|:-----------------:|:--------------------:|:-----------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:------------:|:----------------:|:-------------------:|:----------------:| | 0.1548 | 1.0 | 1471 | 0.1755 | 0.6667 | 0.5217 | 0.5854 | 23 | 0.0 | 0.0 | 0.0 | 2 | 0.5714 | 0.8182 | 0.6729 | 44 | 0.5217 | 0.3429 | 0.4138 | 35 | 0.4103 | 0.4571 | 0.4324 | 35 | 0.0 | 0.0 | 0.0 | 24 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 1 | 0.8645 | 0.8252 | 0.8444 | 286 | 0.0 | 0.0 | 0.0 | 10 | 0.7711 | 0.8965 | 0.8291 | 541 | 0.7627 | 0.7907 | 0.7764 | 0.9582 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.1209 | 2.0 | 2942 | 0.0797 | 0.9130 | 0.9130 | 0.9130 | 23 | 0.0 | 0.0 | 0.0 | 2 | 0.9348 | 0.9773 | 0.9556 | 44 | 0.8462 | 0.6286 | 0.7213 | 35 | 0.8889 | 0.9143 | 0.9014 | 35 | 0.75 | 0.875 | 0.8077 | 24 | 1.0 | 0.4286 | 0.6 | 7 | 0.0 | 0.0 | 0.0 | 1 | 0.8993 | 0.8741 | 0.8865 | 286 | 0.875 | 0.7 | 0.7778 | 10 | 0.9336 | 0.9094 | 0.9213 | 541 | 0.9138 | 0.8839 | 0.8986 | 0.9808 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.0559 | 3.0 | 4413 | 0.0448 | 0.9583 | 1.0 | 0.9787 | 23 | 0.0 | 0.0 | 0.0 | 2 | 0.9773 | 0.9773 | 0.9773 | 44 | 0.9259 | 0.7143 | 0.8065 | 35 | 1.0 | 0.9429 | 0.9706 | 35 | 0.9231 | 1.0 | 0.9600 | 24 | 1.0 | 1.0 | 1.0 | 7 | 1.0 | 1.0 | 1.0 | 1 | 0.9030 | 0.9441 | 0.9231 | 286 | 0.9 | 0.9 | 0.9 | 10 | 0.9484 | 0.9519 | 0.9502 | 541 | 0.9369 | 0.9425 | 0.9397 | 0.9883 | 0.0 | 0.0 | 0.0 | 0.0 | 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