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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilBERT_mergeddata_with_preprocessing_grid_search
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilBERT_mergeddata_with_preprocessing_grid_search
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1986
- Precision: 0.9664
- Recall: 0.9668
- F1: 0.9665
- Accuracy: 0.9667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 225 | 0.1748 | 0.9549 | 0.9514 | 0.9520 | 0.9528 |
| No log | 2.0 | 450 | 0.1584 | 0.9567 | 0.9563 | 0.9562 | 0.9567 |
| 0.291 | 3.0 | 675 | 0.1553 | 0.9622 | 0.9627 | 0.9622 | 0.9622 |
| 0.291 | 4.0 | 900 | 0.1571 | 0.9647 | 0.9651 | 0.9646 | 0.965 |
| 0.0501 | 5.0 | 1125 | 0.1747 | 0.9667 | 0.9671 | 0.9666 | 0.9667 |
| 0.0501 | 6.0 | 1350 | 0.1887 | 0.9650 | 0.9658 | 0.9653 | 0.9656 |
| 0.0111 | 7.0 | 1575 | 0.1862 | 0.9668 | 0.9666 | 0.9665 | 0.9667 |
| 0.0111 | 8.0 | 1800 | 0.1985 | 0.9647 | 0.9649 | 0.9647 | 0.965 |
| 0.0044 | 9.0 | 2025 | 0.1954 | 0.9658 | 0.9662 | 0.9659 | 0.9661 |
| 0.0044 | 10.0 | 2250 | 0.1986 | 0.9664 | 0.9668 | 0.9665 | 0.9667 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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