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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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base_model: cointegrated/rubert-tiny2 |
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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: 0.2041 |
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- Addressee Precision: 0.7273 |
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- Addressee Recall: 0.8 |
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- Addressee F1: 0.7619 |
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- Addressee Number: 10 |
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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: 1 |
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- Causator Precision: 0.8824 |
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- Causator Recall: 0.8333 |
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- Causator F1: 0.8571 |
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- Causator Number: 18 |
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- Cause Precision: 0.6667 |
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- Cause Recall: 0.1538 |
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- Cause F1: 0.25 |
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- Cause Number: 13 |
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- Contrsubject Precision: 0.6667 |
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- Contrsubject Recall: 0.3333 |
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- Contrsubject F1: 0.4444 |
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- Contrsubject Number: 6 |
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- Deliberative Precision: 1.0 |
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- Deliberative Recall: 0.4 |
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- Deliberative F1: 0.5714 |
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- Deliberative Number: 5 |
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- Experiencer Precision: 0.7660 |
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- Experiencer Recall: 0.8 |
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- Experiencer F1: 0.7826 |
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- Experiencer Number: 90 |
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- Object Precision: 0.7576 |
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- Object Recall: 0.6868 |
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- Object F1: 0.7205 |
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- Object Number: 182 |
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- Predicate Precision: 0.9713 |
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- Predicate Recall: 0.9967 |
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- Predicate F1: 0.9839 |
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- Predicate Number: 306 |
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- Overall Precision: 0.8719 |
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- Overall Recall: 0.8415 |
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- Overall F1: 0.8565 |
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- Overall Accuracy: 0.9429 |
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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: 8 |
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- total_train_batch_size: 32 |
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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 | 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 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 | |
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| 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 | |
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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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