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balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch-2

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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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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch-2
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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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+ # balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch-2
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4410
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+ - Precision: 0.9276
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+ - Recall: 0.9141
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+ - F1: 0.9163
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+ - Accuracy: 0.9138
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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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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+ - num_epochs: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 2.8924 | 1.0 | 52 | 2.1510 | 0.3032 | 0.2572 | 0.2464 | 0.4120 |
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+ | 1.8492 | 2.0 | 104 | 1.3548 | 0.5880 | 0.5350 | 0.5046 | 0.6440 |
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+ | 1.1692 | 3.0 | 156 | 0.8621 | 0.7443 | 0.6770 | 0.6775 | 0.7468 |
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+ | 0.7452 | 4.0 | 208 | 0.6391 | 0.7779 | 0.7919 | 0.7691 | 0.8159 |
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+ | 0.5185 | 5.0 | 260 | 0.6401 | 0.8197 | 0.8000 | 0.7868 | 0.8109 |
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+ | 0.3536 | 6.0 | 312 | 0.4251 | 0.8808 | 0.8684 | 0.8675 | 0.8820 |
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+ | 0.2411 | 7.0 | 364 | 0.4748 | 0.8709 | 0.8659 | 0.8613 | 0.8800 |
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+ | 0.1762 | 8.0 | 416 | 0.3809 | 0.8991 | 0.8721 | 0.8812 | 0.8971 |
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+ | 0.1377 | 9.0 | 468 | 0.3977 | 0.9062 | 0.8950 | 0.8953 | 0.9022 |
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+ | 0.1026 | 10.0 | 520 | 0.4637 | 0.9068 | 0.8887 | 0.8897 | 0.8951 |
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+ | 0.0763 | 11.0 | 572 | 0.4210 | 0.9079 | 0.9066 | 0.9020 | 0.9012 |
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+ | 0.0554 | 12.0 | 624 | 0.4950 | 0.8962 | 0.8837 | 0.8809 | 0.8850 |
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+ | 0.0419 | 13.0 | 676 | 0.4643 | 0.9043 | 0.9007 | 0.8969 | 0.8961 |
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+ | 0.0358 | 14.0 | 728 | 0.3718 | 0.9171 | 0.9183 | 0.9144 | 0.9173 |
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+ | 0.0264 | 15.0 | 780 | 0.4456 | 0.9349 | 0.9042 | 0.9132 | 0.9123 |
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+ | 0.0178 | 16.0 | 832 | 0.4296 | 0.9362 | 0.9169 | 0.9227 | 0.9193 |
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+ | 0.015 | 17.0 | 884 | 0.3900 | 0.9302 | 0.9235 | 0.9240 | 0.9254 |
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+ | 0.0105 | 18.0 | 936 | 0.4335 | 0.9284 | 0.9161 | 0.9181 | 0.9168 |
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+ | 0.0116 | 19.0 | 988 | 0.4426 | 0.9285 | 0.9138 | 0.9166 | 0.9138 |
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+ | 0.0104 | 20.0 | 1040 | 0.4410 | 0.9276 | 0.9141 | 0.9163 | 0.9138 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.1
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+ - Pytorch 2.0.0+cu118
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+ - Datasets 2.11.0
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+ - Tokenizers 0.13.3