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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: rubert-base-srl-seqlabeling |
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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-base-srl-seqlabeling |
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This model is a fine-tuned version of [./ruBert-base/](https://huggingface.co/./ruBert-base/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1723 |
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- Causator Precision: 0.8539 |
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- Causator Recall: 0.8352 |
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- Causator F1: 0.8444 |
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- Causator Number: 91 |
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- Expiriencer Precision: 0.9259 |
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- Expiriencer Recall: 0.9740 |
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- Expiriencer F1: 0.9494 |
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- Expiriencer Number: 77 |
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- Instrument Precision: 0.375 |
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- Instrument Recall: 1.0 |
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- Instrument F1: 0.5455 |
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- Instrument Number: 3 |
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- Other Precision: 0.0 |
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- Other Recall: 0.0 |
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- Other F1: 0.0 |
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- Other Number: 1 |
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- Predicate Precision: 0.9352 |
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- Predicate Recall: 0.9902 |
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- Predicate F1: 0.9619 |
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- Predicate Number: 102 |
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- Overall Precision: 0.8916 |
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- Overall Recall: 0.9307 |
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- Overall F1: 0.9107 |
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- Overall Accuracy: 0.9667 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Causator Precision | Causator Recall | Causator F1 | Causator Number | Expiriencer Precision | Expiriencer Recall | Expiriencer F1 | Expiriencer Number | Instrument Precision | Instrument Recall | Instrument F1 | Instrument Number | Other Precision | Other Recall | Other F1 | Other 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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| 0.2552 | 1.0 | 56 | 0.3471 | 0.8841 | 0.6703 | 0.7625 | 91 | 0.8421 | 0.8312 | 0.8366 | 77 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9259 | 0.9804 | 0.9524 | 102 | 0.8893 | 0.8212 | 0.8539 | 0.9203 | |
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| 0.2385 | 2.0 | 112 | 0.1608 | 0.9103 | 0.7802 | 0.8402 | 91 | 0.9375 | 0.9740 | 0.9554 | 77 | 0.2857 | 0.6667 | 0.4 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9519 | 0.9706 | 0.9612 | 102 | 0.9182 | 0.9015 | 0.9098 | 0.9554 | |
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| 0.0367 | 3.0 | 168 | 0.1311 | 0.8902 | 0.8022 | 0.8439 | 91 | 0.9375 | 0.9740 | 0.9554 | 77 | 0.4286 | 1.0 | 0.6 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9709 | 0.9804 | 0.9756 | 102 | 0.9228 | 0.9161 | 0.9194 | 0.9673 | |
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| 0.0494 | 4.0 | 224 | 0.1507 | 0.7812 | 0.8242 | 0.8021 | 91 | 0.9241 | 0.9481 | 0.9359 | 77 | 0.4286 | 1.0 | 0.6 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9524 | 0.9804 | 0.9662 | 102 | 0.8746 | 0.9161 | 0.8948 | 0.9637 | |
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| 0.0699 | 5.0 | 280 | 0.1830 | 0.8276 | 0.7912 | 0.8090 | 91 | 0.8941 | 0.9870 | 0.9383 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.875 | 0.9197 | 0.8968 | 0.9560 | |
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| 0.0352 | 6.0 | 336 | 0.1994 | 0.7857 | 0.8462 | 0.8148 | 91 | 0.9048 | 0.9870 | 0.9441 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9266 | 0.9902 | 0.9573 | 102 | 0.8595 | 0.9380 | 0.8970 | 0.9572 | |
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| 0.0186 | 7.0 | 392 | 0.1657 | 0.8652 | 0.8462 | 0.8556 | 91 | 0.9146 | 0.9740 | 0.9434 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8920 | 0.9343 | 0.9127 | 0.9673 | |
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| 0.0052 | 8.0 | 448 | 0.1716 | 0.8556 | 0.8462 | 0.8508 | 91 | 0.9259 | 0.9740 | 0.9494 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8920 | 0.9343 | 0.9127 | 0.9673 | |
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| 0.0094 | 9.0 | 504 | 0.1715 | 0.8444 | 0.8352 | 0.8398 | 91 | 0.9259 | 0.9740 | 0.9494 | 77 | 0.4286 | 1.0 | 0.6 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8916 | 0.9307 | 0.9107 | 0.9667 | |
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| 0.0078 | 10.0 | 560 | 0.1723 | 0.8539 | 0.8352 | 0.8444 | 91 | 0.9259 | 0.9740 | 0.9494 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8916 | 0.9307 | 0.9107 | 0.9667 | |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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