---
license: mit
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: 0.2041
- Addressee Precision: 0.7273
- Addressee Recall: 0.8
- Addressee F1: 0.7619
- Addressee Number: 10
- Benefactive Precision: 0.0
- Benefactive Recall: 0.0
- Benefactive F1: 0.0
- Benefactive Number: 1
- Causator Precision: 0.8824
- Causator Recall: 0.8333
- Causator F1: 0.8571
- Causator Number: 18
- Cause Precision: 0.6667
- Cause Recall: 0.1538
- Cause F1: 0.25
- Cause Number: 13
- Contrsubject Precision: 0.6667
- Contrsubject Recall: 0.3333
- Contrsubject F1: 0.4444
- Contrsubject Number: 6
- Deliberative Precision: 1.0
- Deliberative Recall: 0.4
- Deliberative F1: 0.5714
- Deliberative Number: 5
- Experiencer Precision: 0.7660
- Experiencer Recall: 0.8
- Experiencer F1: 0.7826
- Experiencer Number: 90
- Object Precision: 0.7576
- Object Recall: 0.6868
- Object F1: 0.7205
- Object Number: 182
- Predicate Precision: 0.9713
- Predicate Recall: 0.9967
- Predicate F1: 0.9839
- Predicate Number: 306
- Overall Precision: 0.8719
- Overall Recall: 0.8415
- Overall F1: 0.8565
- Overall Accuracy: 0.9429

## 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: 0.00018632464179881193
- train_batch_size: 4
- eval_batch_size: 1
- seed: 755657
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2
- 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 | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Object Precision | Object Recall | Object F1 | Object Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2845        | 1.0   | 181  | 0.2356          | 0.8                 | 0.8              | 0.8000       | 10               | 0.0                   | 0.0                | 0.0            | 1                  | 0.7895             | 0.8333          | 0.8108      | 18              | 0.0             | 0.0          | 0.0      | 13           | 0.0                    | 0.0                 | 0.0             | 6                   | 0.0                    | 0.0                 | 0.0             | 5                   | 0.7320                | 0.7889             | 0.7594         | 90                 | 0.7740           | 0.6209        | 0.6890    | 182           | 0.9744              | 0.9935           | 0.9838       | 306              | 0.875             | 0.8098         | 0.8412     | 0.9376           |
| 0.1875        | 1.99  | 362  | 0.2041          | 0.7273              | 0.8              | 0.7619       | 10               | 0.0                   | 0.0                | 0.0            | 1                  | 0.8824             | 0.8333          | 0.8571      | 18              | 0.6667          | 0.1538       | 0.25     | 13           | 0.6667                 | 0.3333              | 0.4444          | 6                   | 1.0                    | 0.4                 | 0.5714          | 5                   | 0.7660                | 0.8                | 0.7826         | 90                 | 0.7576           | 0.6868        | 0.7205    | 182           | 0.9713              | 0.9967           | 0.9839       | 306              | 0.8719            | 0.8415         | 0.8565     | 0.9429           |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3