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favs-filtersort-multilabel-classification-bert-base-cased

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - filter_sort
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+ metrics:
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: favs-filtersort-multilabel-classification-bert-base-cased
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: filter_sort
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+ type: filter_sort
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.823529411764706
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.0
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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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+ # favs-filtersort-multilabel-classification-bert-base-cased
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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 filter_sort dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4283
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+ - F1: 0.8235
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+ - Roc Auc: 0.9058
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+ - Accuracy: 0.0
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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: 1.5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) 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 | F1 | Roc Auc | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | No log | 1.0 | 9 | 0.5901 | 0.6087 | 0.7289 | 0.0 |
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+ | 0.665 | 2.0 | 18 | 0.5667 | 0.4545 | 0.6201 | 0.0 |
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+ | 0.6099 | 3.0 | 27 | 0.5573 | 0.5 | 0.6558 | 0.0 |
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+ | 0.5463 | 4.0 | 36 | 0.5561 | 0.3889 | 0.5966 | 0.0 |
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+ | 0.5071 | 5.0 | 45 | 0.5484 | 0.3889 | 0.5966 | 0.0 |
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+ | 0.4669 | 6.0 | 54 | 0.5462 | 0.4324 | 0.6193 | 0.0 |
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+ | 0.4371 | 7.0 | 63 | 0.5326 | 0.4737 | 0.6420 | 0.0 |
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+ | 0.4145 | 8.0 | 72 | 0.5202 | 0.5854 | 0.7102 | 0.0 |
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+ | 0.3959 | 9.0 | 81 | 0.5020 | 0.6364 | 0.7468 | 0.0 |
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+ | 0.3733 | 10.0 | 90 | 0.4944 | 0.6364 | 0.7468 | 0.0 |
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+ | 0.3733 | 11.0 | 99 | 0.4675 | 0.7234 | 0.8149 | 0.0 |
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+ | 0.3622 | 12.0 | 108 | 0.4626 | 0.7843 | 0.8742 | 0.0 |
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+ | 0.3382 | 13.0 | 117 | 0.4499 | 0.8077 | 0.8969 | 0.0 |
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+ | 0.3341 | 14.0 | 126 | 0.4482 | 0.8077 | 0.8969 | 0.0 |
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+ | 0.315 | 15.0 | 135 | 0.4332 | 0.8077 | 0.8969 | 0.0 |
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+ | 0.3253 | 16.0 | 144 | 0.4283 | 0.8235 | 0.9058 | 0.0 |
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+ | 0.3031 | 17.0 | 153 | 0.4246 | 0.8077 | 0.8969 | 0.0 |
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+ | 0.3071 | 18.0 | 162 | 0.4155 | 0.8077 | 0.8969 | 0.0 |
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+ | 0.2944 | 19.0 | 171 | 0.4129 | 0.8077 | 0.8969 | 0.0 |
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+ | 0.2996 | 20.0 | 180 | 0.4129 | 0.8077 | 0.8969 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.1
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+ - Pytorch 1.12.1
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1