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---
license: mit
base_model: ai-forever/ruElectra-medium
tags:
- generated_from_trainer
model-index:
- name: rubert-electra-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-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
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