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autosyrup
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bert

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
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+ ---
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+ license: apache-2.0
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+ base_model: bert-base-cased
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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: bert
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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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+ # bert
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+
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3752
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+ - Precision: 0.5495
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+ - Recall: 0.5949
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+ - F1: 0.5713
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+ - Accuracy: 0.9455
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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: 0.0001
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+ - train_batch_size: 32
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.99) 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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+ | No log | 1.0 | 151 | 0.1826 | 0.4095 | 0.4084 | 0.4089 | 0.9362 |
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+ | No log | 2.0 | 302 | 0.1684 | 0.4941 | 0.5303 | 0.5116 | 0.9442 |
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+ | No log | 3.0 | 453 | 0.2528 | 0.5197 | 0.4477 | 0.4810 | 0.9398 |
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+ | 0.1001 | 4.0 | 604 | 0.2100 | 0.5182 | 0.5583 | 0.5375 | 0.9439 |
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+ | 0.1001 | 5.0 | 755 | 0.2556 | 0.5207 | 0.4783 | 0.4986 | 0.9419 |
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+ | 0.1001 | 6.0 | 906 | 0.2908 | 0.4132 | 0.4204 | 0.4168 | 0.9365 |
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+ | 0.0205 | 7.0 | 1057 | 0.3046 | 0.5 | 0.6236 | 0.5550 | 0.9435 |
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+ | 0.0205 | 8.0 | 1208 | 0.3057 | 0.5324 | 0.5750 | 0.5529 | 0.9458 |
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+ | 0.0205 | 9.0 | 1359 | 0.3122 | 0.5626 | 0.5776 | 0.5700 | 0.9469 |
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+ | 0.0082 | 10.0 | 1510 | 0.3673 | 0.5733 | 0.5263 | 0.5488 | 0.9441 |
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+ | 0.0082 | 11.0 | 1661 | 0.3432 | 0.5482 | 0.5270 | 0.5374 | 0.9455 |
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+ | 0.0082 | 12.0 | 1812 | 0.3305 | 0.5590 | 0.5716 | 0.5652 | 0.9445 |
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+ | 0.0082 | 13.0 | 1963 | 0.3293 | 0.5434 | 0.6009 | 0.5707 | 0.9431 |
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+ | 0.005 | 14.0 | 2114 | 0.4080 | 0.5627 | 0.5803 | 0.5713 | 0.9451 |
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+ | 0.005 | 15.0 | 2265 | 0.3752 | 0.5495 | 0.5949 | 0.5713 | 0.9455 |
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+ | 0.005 | 16.0 | 2416 | 0.4140 | 0.5823 | 0.5470 | 0.5641 | 0.9455 |
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+ | 0.002 | 17.0 | 2567 | 0.4308 | 0.5555 | 0.5670 | 0.5612 | 0.9438 |
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+ | 0.002 | 18.0 | 2718 | 0.4389 | 0.5594 | 0.5676 | 0.5635 | 0.9436 |
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+ | 0.002 | 19.0 | 2869 | 0.4463 | 0.5609 | 0.5676 | 0.5642 | 0.9444 |
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+ | 0.0007 | 20.0 | 3020 | 0.4512 | 0.5648 | 0.5636 | 0.5642 | 0.9448 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.14.2
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+ - Tokenizers 0.13.3