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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: 0.2182
- Benefactive Precision: 0.0
- Benefactive Recall: 0.0
- Benefactive F1: 0.0
- Benefactive Number: 2
- Causator Precision: 0.7333
- Causator Recall: 0.9167
- Causator F1: 0.8148
- Causator Number: 12
- Cause Precision: 0.5
- Cause Recall: 0.2
- Cause F1: 0.2857
- Cause Number: 5
- Contrsubject Precision: 1.0
- Contrsubject Recall: 0.2222
- Contrsubject F1: 0.3636
- Contrsubject Number: 9
- Deliberative Precision: 1.0
- Deliberative Recall: 0.25
- Deliberative F1: 0.4
- Deliberative Number: 4
- Experiencer Precision: 0.7108
- Experiencer Recall: 0.7468
- Experiencer F1: 0.7284
- Experiencer Number: 79
- Object Precision: 0.6966
- Object Recall: 0.6779
- Object F1: 0.6871
- Object Number: 149
- Predicate Precision: 0.9847
- Predicate Recall: 0.9923
- Predicate F1: 0.9885
- Predicate Number: 260
- Overall Precision: 0.8490
- Overall Recall: 0.8327
- Overall F1: 0.8408
- Overall Accuracy: 0.9274
## 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 | 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2886 | 1.0 | 152 | 0.2507 | 0.0 | 0.0 | 0.0 | 2 | 0.6667 | 0.8333 | 0.7407 | 12 | 1.0 | 0.2 | 0.3333 | 5 | 1.0 | 0.1111 | 0.2000 | 9 | 0.0 | 0.0 | 0.0 | 4 | 0.6596 | 0.7848 | 0.7168 | 79 | 0.7232 | 0.5436 | 0.6207 | 149 | 0.9847 | 0.9885 | 0.9866 | 260 | 0.8512 | 0.7923 | 0.8207 | 0.9203 |
| 0.1911 | 2.0 | 304 | 0.2182 | 0.0 | 0.0 | 0.0 | 2 | 0.7333 | 0.9167 | 0.8148 | 12 | 0.5 | 0.2 | 0.2857 | 5 | 1.0 | 0.2222 | 0.3636 | 9 | 1.0 | 0.25 | 0.4 | 4 | 0.7108 | 0.7468 | 0.7284 | 79 | 0.6966 | 0.6779 | 0.6871 | 149 | 0.9847 | 0.9923 | 0.9885 | 260 | 0.8490 | 0.8327 | 0.8408 | 0.9274 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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