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---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_large_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. -->
# BERT_large_with_preprocessing_grid_search
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0365
- Precision: 0.8410
- Recall: 0.8308
- F1: 0.8352
- Accuracy: 0.8753
## 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: 2e-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9616 | 1.0 | 510 | 0.6482 | 0.7704 | 0.8009 | 0.7781 | 0.8360 |
| 0.4395 | 2.0 | 1020 | 0.7507 | 0.8422 | 0.7993 | 0.8157 | 0.8552 |
| 0.2995 | 3.0 | 1530 | 0.7064 | 0.8445 | 0.8213 | 0.8287 | 0.8684 |
| 0.2117 | 4.0 | 2040 | 0.7889 | 0.8262 | 0.8325 | 0.8245 | 0.8679 |
| 0.1805 | 5.0 | 2550 | 0.9295 | 0.8406 | 0.8161 | 0.8271 | 0.8670 |
| 0.1225 | 6.0 | 3060 | 0.9491 | 0.8429 | 0.8260 | 0.8333 | 0.8758 |
| 0.0983 | 7.0 | 3570 | 0.9901 | 0.8444 | 0.8299 | 0.8359 | 0.8773 |
| 0.0869 | 8.0 | 4080 | 1.0300 | 0.8377 | 0.8278 | 0.8319 | 0.8719 |
| 0.0745 | 9.0 | 4590 | 1.0220 | 0.8439 | 0.8341 | 0.8379 | 0.8773 |
| 0.0591 | 10.0 | 5100 | 1.0365 | 0.8410 | 0.8308 | 0.8352 | 0.8753 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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