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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: rubert-tiny2-srl |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# rubert-tiny2-srl |
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This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2006 |
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- Benefactive Precision: 0.0 |
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- Benefactive Recall: 0.0 |
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- Benefactive F1: 0.0 |
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- Benefactive Number: 2 |
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- Causator Precision: 0.8571 |
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- Causator Recall: 1.0 |
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- Causator F1: 0.9231 |
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- Causator Number: 12 |
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- Cause Precision: 1.0 |
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- Cause Recall: 0.2 |
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- Cause F1: 0.3333 |
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- Cause Number: 5 |
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- Contrsubject Precision: 0.6 |
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- Contrsubject Recall: 0.3333 |
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- Contrsubject F1: 0.4286 |
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- Contrsubject Number: 9 |
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- Deliberative Precision: 0.8 |
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- Deliberative Recall: 1.0 |
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- Deliberative F1: 0.8889 |
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- Deliberative Number: 4 |
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- Experiencer Precision: 0.7160 |
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- Experiencer Recall: 0.7342 |
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- Experiencer F1: 0.7250 |
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- Experiencer Number: 79 |
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- Object Precision: 0.7203 |
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- Object Recall: 0.6913 |
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- Object F1: 0.7055 |
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- Object Number: 149 |
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- Predicate Precision: 0.9847 |
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- Predicate Recall: 0.9923 |
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- Predicate F1: 0.9885 |
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- Predicate Number: 260 |
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- Overall Precision: 0.8591 |
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- Overall Recall: 0.8442 |
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- Overall F1: 0.8516 |
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- Overall Accuracy: 0.9331 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.00018632464179881193 |
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- train_batch_size: 4 |
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- eval_batch_size: 1 |
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- seed: 755657 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.02 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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