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---
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license: apache-2.0
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base_model: distilbert-base-uncased
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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: distilBERT_mergeddata_with_preprocessing_grid_search
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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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# distilBERT_mergeddata_with_preprocessing_grid_search
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2308
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- Precision: 0.9670
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- Recall: 0.9671
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- F1: 0.9669
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- Accuracy: 0.9672
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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: 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: 10
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### Training results
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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 | 450 | 0.1748 | 0.9542 | 0.9540 | 0.9536 | 0.9539 |
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| 0.4081 | 2.0 | 900 | 0.1899 | 0.9575 | 0.9577 | 0.9569 | 0.9578 |
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| 0.1208 | 3.0 | 1350 | 0.1722 | 0.9648 | 0.9649 | 0.9645 | 0.9644 |
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| 0.0671 | 4.0 | 1800 | 0.1987 | 0.9616 | 0.9626 | 0.9617 | 0.9617 |
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| 0.0362 | 5.0 | 2250 | 0.2093 | 0.9618 | 0.9623 | 0.9620 | 0.9622 |
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| 0.0211 | 6.0 | 2700 | 0.2021 | 0.9681 | 0.9681 | 0.9679 | 0.9683 |
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| 0.0045 | 7.0 | 3150 | 0.2268 | 0.9642 | 0.9641 | 0.9639 | 0.9644 |
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| 0.0027 | 8.0 | 3600 | 0.2255 | 0.9675 | 0.9677 | 0.9675 | 0.9678 |
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| 0.0022 | 9.0 | 4050 | 0.2292 | 0.9675 | 0.9676 | 0.9674 | 0.9678 |
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| 0.0009 | 10.0 | 4500 | 0.2308 | 0.9670 | 0.9671 | 0.9669 | 0.9672 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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