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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: 0.3357 |
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- Benefactive Precision: 1.0 |
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- Benefactive Recall: 0.5 |
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- Benefactive F1: 0.6667 |
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- Benefactive Number: 2 |
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- Causator Precision: 1.0 |
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- Causator Recall: 1.0 |
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- Causator F1: 1.0 |
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- Causator Number: 12 |
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- Cause Precision: 0.4 |
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- Cause Recall: 0.4 |
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- Cause F1: 0.4000 |
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- Cause Number: 5 |
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- Contrsubject Precision: 0.75 |
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- Contrsubject Recall: 0.6667 |
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- Contrsubject F1: 0.7059 |
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- Contrsubject Number: 9 |
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- Deliberative Precision: 1.0 |
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- Deliberative Recall: 1.0 |
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- Deliberative F1: 1.0 |
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- Deliberative Number: 4 |
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- Experiencer Precision: 0.7442 |
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- Experiencer Recall: 0.8101 |
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- Experiencer F1: 0.7758 |
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- Experiencer Number: 79 |
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- Object Precision: 0.7551 |
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- Object Recall: 0.7450 |
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- Object F1: 0.7500 |
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- Object Number: 149 |
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- Predicate Precision: 0.9809 |
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- Predicate Recall: 0.9885 |
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- Predicate F1: 0.9847 |
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- Predicate Number: 260 |
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- Overall Precision: 0.8705 |
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- Overall Recall: 0.8788 |
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- Overall F1: 0.8746 |
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- Overall Accuracy: 0.9411 |
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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: 0.00010372880304918982 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 923789 |
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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: 0.29 |
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- num_epochs: 5 |
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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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| 0.3041 | 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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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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