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rubert-base-srl-seqlabeling

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bert
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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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+
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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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+
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+ # rubert-base-srl-seqlabeling
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+
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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: 0.2417
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+ - Predicate Precision: 0.9323
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+ - Predicate Recall: 0.9612
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+ - Predicate F1: 0.9466
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+ - Predicate Number: 129
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+ - Инструмент Precision: 0.0
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+ - Инструмент Recall: 0.0
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+ - Инструмент F1: 0.0
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+ - Инструмент Number: 1
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+ - Каузатор Precision: 0.7667
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+ - Каузатор Recall: 0.6301
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+ - Каузатор F1: 0.6917
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+ - Каузатор Number: 73
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+ - Экспериенцер Precision: 0.6939
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+ - Экспериенцер Recall: 0.8293
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+ - Экспериенцер F1: 0.7556
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+ - Экспериенцер Number: 41
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+ - Overall Precision: 0.8430
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+ - Overall Recall: 0.8361
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+ - Overall F1: 0.8395
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+ - Overall Accuracy: 0.9584
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Инструмент Precision | Инструмент Recall | Инструмент F1 | Инструмент Number | Каузатор Precision | Каузатор Recall | Каузатор F1 | Каузатор Number | Экспериенцер Precision | Экспериенцер Recall | Экспериенцер F1 | Экспериенцер Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.2462 | 1.0 | 54 | 0.1554 | 0.8897 | 1.0 | 0.9416 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.7708 | 0.5068 | 0.6116 | 73 | 0.6047 | 0.6341 | 0.6190 | 41 | 0.8136 | 0.7869 | 0.8 | 0.9486 |
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+ | 0.1863 | 2.0 | 108 | 0.1268 | 0.9014 | 0.9922 | 0.9446 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.8444 | 0.5205 | 0.6441 | 73 | 0.6829 | 0.6829 | 0.6829 | 41 | 0.8509 | 0.7951 | 0.8220 | 0.9557 |
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+ | 0.0668 | 3.0 | 162 | 0.1288 | 0.9338 | 0.9845 | 0.9585 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.8148 | 0.6027 | 0.6929 | 73 | 0.6957 | 0.7805 | 0.7356 | 41 | 0.8602 | 0.8320 | 0.8458 | 0.9600 |
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+ | 0.039 | 4.0 | 216 | 0.1695 | 0.9007 | 0.9845 | 0.9407 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.8298 | 0.5342 | 0.6500 | 73 | 0.6441 | 0.9268 | 0.76 | 41 | 0.8259 | 0.8361 | 0.8310 | 0.9557 |
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+ | 0.0187 | 5.0 | 270 | 0.1955 | 0.9323 | 0.9612 | 0.9466 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.75 | 0.5753 | 0.6512 | 73 | 0.7105 | 0.6585 | 0.6835 | 41 | 0.8502 | 0.7910 | 0.8195 | 0.9551 |
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+ | 0.0216 | 6.0 | 324 | 0.2083 | 0.9394 | 0.9612 | 0.9502 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.7586 | 0.6027 | 0.6718 | 73 | 0.6829 | 0.6829 | 0.6829 | 41 | 0.8485 | 0.8033 | 0.8253 | 0.9562 |
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+ | 0.0176 | 7.0 | 378 | 0.2203 | 0.9323 | 0.9612 | 0.9466 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.7273 | 0.6575 | 0.6906 | 73 | 0.68 | 0.8293 | 0.7473 | 41 | 0.8273 | 0.8443 | 0.8357 | 0.9578 |
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+ | 0.0037 | 8.0 | 432 | 0.2313 | 0.9323 | 0.9612 | 0.9466 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.7541 | 0.6301 | 0.6866 | 73 | 0.6809 | 0.7805 | 0.7273 | 41 | 0.8382 | 0.8279 | 0.8330 | 0.9567 |
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+ | 0.0089 | 9.0 | 486 | 0.2409 | 0.9323 | 0.9612 | 0.9466 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.7705 | 0.6438 | 0.7015 | 73 | 0.6939 | 0.8293 | 0.7556 | 41 | 0.8436 | 0.8402 | 0.8419 | 0.9589 |
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+ | 0.0043 | 10.0 | 540 | 0.2417 | 0.9323 | 0.9612 | 0.9466 | 129 | 0.0 | 0.0 | 0.0 | 1 | 0.7667 | 0.6301 | 0.6917 | 73 | 0.6939 | 0.8293 | 0.7556 | 41 | 0.8430 | 0.8361 | 0.8395 | 0.9584 |
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+
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+
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+ ### Framework versions
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+
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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