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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: VF_BERT_ST_1800_V2
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. -->
# VF_BERT_ST_1800_V2
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1698
- Precision: 0.9686
- Recall: 0.9772
- F1: 0.9729
- Accuracy: 0.9683
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2128 | 1.0 | 569 | 0.1128 | 0.9644 | 0.9733 | 0.9688 | 0.9654 |
| 0.0784 | 2.0 | 1138 | 0.1145 | 0.9668 | 0.9753 | 0.9710 | 0.9672 |
| 0.0512 | 3.0 | 1707 | 0.1242 | 0.9680 | 0.9746 | 0.9712 | 0.9663 |
| 0.0327 | 4.0 | 2276 | 0.1227 | 0.9706 | 0.9762 | 0.9734 | 0.9673 |
| 0.022 | 5.0 | 2845 | 0.1298 | 0.9684 | 0.9755 | 0.9719 | 0.9686 |
| 0.0153 | 6.0 | 3414 | 0.1410 | 0.9710 | 0.9778 | 0.9744 | 0.9698 |
| 0.0118 | 7.0 | 3983 | 0.1589 | 0.9681 | 0.9777 | 0.9729 | 0.9686 |
| 0.0058 | 8.0 | 4552 | 0.1617 | 0.9696 | 0.9773 | 0.9735 | 0.9691 |
| 0.005 | 9.0 | 5121 | 0.1731 | 0.9685 | 0.9773 | 0.9729 | 0.9683 |
| 0.0043 | 10.0 | 5690 | 0.1698 | 0.9686 | 0.9772 | 0.9729 | 0.9683 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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