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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_without_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_without_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: 0.7575
- Precision: 0.8533
- Recall: 0.8477
- F1: 0.8486
- Accuracy: 0.8847
## 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: 3e-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 | 257 | 0.5635 | 0.7528 | 0.8373 | 0.7788 | 0.8439 |
| 0.7607 | 2.0 | 514 | 0.5324 | 0.8060 | 0.8314 | 0.8098 | 0.8648 |
| 0.7607 | 3.0 | 771 | 0.5216 | 0.8152 | 0.8475 | 0.8265 | 0.8765 |
| 0.2593 | 4.0 | 1028 | 0.5493 | 0.8179 | 0.8585 | 0.8348 | 0.8823 |
| 0.2593 | 5.0 | 1285 | 0.6226 | 0.8220 | 0.8419 | 0.8308 | 0.8794 |
| 0.1473 | 6.0 | 1542 | 0.6677 | 0.8429 | 0.8485 | 0.8442 | 0.8818 |
| 0.1473 | 7.0 | 1799 | 0.6611 | 0.8316 | 0.8481 | 0.8381 | 0.8823 |
| 0.096 | 8.0 | 2056 | 0.7404 | 0.8528 | 0.8448 | 0.8478 | 0.8857 |
| 0.096 | 9.0 | 2313 | 0.7401 | 0.8531 | 0.8476 | 0.8484 | 0.8862 |
| 0.0642 | 10.0 | 2570 | 0.7575 | 0.8533 | 0.8477 | 0.8486 | 0.8847 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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