---
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: 0.2006
- Benefactive Precision: 0.0
- Benefactive Recall: 0.0
- Benefactive F1: 0.0
- Benefactive Number: 2
- Causator Precision: 0.8571
- Causator Recall: 1.0
- Causator F1: 0.9231
- Causator Number: 12
- Cause Precision: 1.0
- Cause Recall: 0.2
- Cause F1: 0.3333
- Cause Number: 5
- Contrsubject Precision: 0.6
- Contrsubject Recall: 0.3333
- Contrsubject F1: 0.4286
- Contrsubject Number: 9
- Deliberative Precision: 0.8
- Deliberative Recall: 1.0
- Deliberative F1: 0.8889
- Deliberative Number: 4
- Experiencer Precision: 0.7160
- Experiencer Recall: 0.7342
- Experiencer F1: 0.7250
- Experiencer Number: 79
- Object Precision: 0.7203
- Object Recall: 0.6913
- Object F1: 0.7055
- Object Number: 149
- Predicate Precision: 0.9847
- Predicate Recall: 0.9923
- Predicate F1: 0.9885
- Predicate Number: 260
- Overall Precision: 0.8591
- Overall Recall: 0.8442
- Overall F1: 0.8516
- Overall Accuracy: 0.9331

## 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: 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: 0.02
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2606        | 1.0   | 304  | 0.2313          | 0.0                   | 0.0                | 0.0            | 2                  | 0.7143             | 0.8333          | 0.7692      | 12              | 0.5             | 0.2          | 0.2857   | 5            | 1.0                    | 0.2222              | 0.3636          | 9                   | 1.0                    | 0.25                | 0.4             | 4                   | 0.8372                | 0.4557             | 0.5902         | 79                 | 0.8              | 0.5101        | 0.6230    | 149           | 0.9846              | 0.9846           | 0.9846       | 260              | 0.9161            | 0.7346         | 0.8154     | 0.9217           |
| 0.1565        | 2.0   | 608  | 0.2006          | 0.0                   | 0.0                | 0.0            | 2                  | 0.8571             | 1.0             | 0.9231      | 12              | 1.0             | 0.2          | 0.3333   | 5            | 0.6                    | 0.3333              | 0.4286          | 9                   | 0.8                    | 1.0                 | 0.8889          | 4                   | 0.7160                | 0.7342             | 0.7250         | 79                 | 0.7203           | 0.6913        | 0.7055    | 149           | 0.9847              | 0.9923           | 0.9885       | 260              | 0.8591            | 0.8442         | 0.8516     | 0.9331           |


### Framework versions

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