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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: 0.7297
- Precision: 0.8417
- Recall: 0.8510
- F1: 0.8460
- Accuracy: 0.8793
## 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 | 257 | 0.4958 | 0.7757 | 0.8526 | 0.8027 | 0.8526 |
| 0.6647 | 2.0 | 514 | 0.4756 | 0.8336 | 0.8480 | 0.8386 | 0.8701 |
| 0.6647 | 3.0 | 771 | 0.4823 | 0.8197 | 0.8588 | 0.8360 | 0.8730 |
| 0.2305 | 4.0 | 1028 | 0.5479 | 0.8314 | 0.8618 | 0.8439 | 0.8735 |
| 0.2305 | 5.0 | 1285 | 0.5832 | 0.8295 | 0.8542 | 0.8401 | 0.8779 |
| 0.1282 | 6.0 | 1542 | 0.5929 | 0.8251 | 0.8627 | 0.8404 | 0.8745 |
| 0.1282 | 7.0 | 1799 | 0.7066 | 0.8476 | 0.8496 | 0.8472 | 0.8774 |
| 0.0828 | 8.0 | 2056 | 0.6873 | 0.8392 | 0.8510 | 0.8448 | 0.8764 |
| 0.0828 | 9.0 | 2313 | 0.7189 | 0.8410 | 0.8524 | 0.8461 | 0.8788 |
| 0.0566 | 10.0 | 2570 | 0.7297 | 0.8417 | 0.8510 | 0.8460 | 0.8793 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|