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---
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: 0.3357
- Benefactive Precision: 1.0
- Benefactive Recall: 0.5
- Benefactive F1: 0.6667
- Benefactive Number: 2
- Causator Precision: 1.0
- Causator Recall: 1.0
- Causator F1: 1.0
- Causator Number: 12
- Cause Precision: 0.4
- Cause Recall: 0.4
- Cause F1: 0.4000
- Cause Number: 5
- Contrsubject Precision: 0.75
- Contrsubject Recall: 0.6667
- Contrsubject F1: 0.7059
- Contrsubject Number: 9
- Deliberative Precision: 1.0
- Deliberative Recall: 1.0
- Deliberative F1: 1.0
- Deliberative Number: 4
- Experiencer Precision: 0.7442
- Experiencer Recall: 0.8101
- Experiencer F1: 0.7758
- Experiencer Number: 79
- Object Precision: 0.7551
- Object Recall: 0.7450
- Object F1: 0.7500
- Object Number: 149
- Predicate Precision: 0.9809
- Predicate Recall: 0.9885
- Predicate F1: 0.9847
- Predicate Number: 260
- Overall Precision: 0.8705
- Overall Recall: 0.8788
- Overall F1: 0.8746
- Overall Accuracy: 0.9411
## 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: 0.00010372880304918982
- train_batch_size: 1
- eval_batch_size: 1
- seed: 923789
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.29
- num_epochs: 5
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3041 | 1.0 | 4864 | 0.3394 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 0.4167 | 0.5882 | 12 | 0.0 | 0.0 | 0.0 | 5 | 1.0 | 0.1111 | 0.2000 | 9 | 0.0 | 0.0 | 0.0 | 4 | 0.8372 | 0.4557 | 0.5902 | 79 | 0.8730 | 0.3691 | 0.5189 | 149 | 0.9884 | 0.9846 | 0.9865 | 260 | 0.9515 | 0.6788 | 0.7924 | 0.9141 |
| 0.3178 | 2.0 | 9728 | 0.2692 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 0.2 | 0.3333 | 5 | 1.0 | 0.2222 | 0.3636 | 9 | 1.0 | 0.5 | 0.6667 | 4 | 0.7403 | 0.7215 | 0.7308 | 79 | 0.8523 | 0.5034 | 0.6329 | 149 | 0.9808 | 0.9846 | 0.9827 | 260 | 0.9142 | 0.7788 | 0.8411 | 0.9321 |
| 0.124 | 3.0 | 14592 | 0.2990 | 1.0 | 0.5 | 0.6667 | 2 | 1.0 | 1.0 | 1.0 | 12 | 0.0 | 0.0 | 0.0 | 5 | 0.75 | 0.6667 | 0.7059 | 9 | 1.0 | 0.5 | 0.6667 | 4 | 0.7386 | 0.8228 | 0.7784 | 79 | 0.8 | 0.6980 | 0.7455 | 149 | 0.9885 | 0.9885 | 0.9885 | 260 | 0.8904 | 0.8596 | 0.8748 | 0.9435 |
| 0.104 | 4.0 | 19456 | 0.2852 | 1.0 | 0.5 | 0.6667 | 2 | 0.9231 | 1.0 | 0.9600 | 12 | 0.4286 | 0.6 | 0.5 | 5 | 0.6 | 0.6667 | 0.6316 | 9 | 1.0 | 0.75 | 0.8571 | 4 | 0.7253 | 0.8354 | 0.7765 | 79 | 0.7044 | 0.7517 | 0.7273 | 149 | 0.9847 | 0.9885 | 0.9866 | 260 | 0.8440 | 0.8846 | 0.8638 | 0.9359 |
| 0.0918 | 5.0 | 24320 | 0.3357 | 1.0 | 0.5 | 0.6667 | 2 | 1.0 | 1.0 | 1.0 | 12 | 0.4 | 0.4 | 0.4000 | 5 | 0.75 | 0.6667 | 0.7059 | 9 | 1.0 | 1.0 | 1.0 | 4 | 0.7442 | 0.8101 | 0.7758 | 79 | 0.7551 | 0.7450 | 0.7500 | 149 | 0.9809 | 0.9885 | 0.9847 | 260 | 0.8705 | 0.8788 | 0.8746 | 0.9411 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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