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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: 0.8262
- Precision: 0.8491
- Recall: 0.8536
- F1: 0.8511
- Accuracy: 0.8837
## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8922 | 1.0 | 514 | 0.5350 | 0.7953 | 0.8363 | 0.8092 | 0.8628 |
| 0.4521 | 2.0 | 1028 | 0.5359 | 0.8214 | 0.8385 | 0.8282 | 0.8652 |
| 0.2928 | 3.0 | 1542 | 0.5876 | 0.8264 | 0.8504 | 0.8367 | 0.8798 |
| 0.2099 | 4.0 | 2056 | 0.6974 | 0.8288 | 0.8435 | 0.8351 | 0.8764 |
| 0.1531 | 5.0 | 2570 | 0.8245 | 0.8367 | 0.8125 | 0.8232 | 0.8710 |
| 0.1124 | 6.0 | 3084 | 0.7553 | 0.8349 | 0.8543 | 0.8435 | 0.8764 |
| 0.1045 | 7.0 | 3598 | 0.7912 | 0.8452 | 0.8538 | 0.8492 | 0.8822 |
| 0.0716 | 8.0 | 4112 | 0.7909 | 0.8422 | 0.8529 | 0.8471 | 0.8788 |
| 0.0746 | 9.0 | 4626 | 0.8364 | 0.8462 | 0.8458 | 0.8458 | 0.8779 |
| 0.0533 | 10.0 | 5140 | 0.8262 | 0.8491 | 0.8536 | 0.8511 | 0.8837 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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