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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-base-cased-finetuned-filtered-0609
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-base-cased-finetuned-filtered-0609
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2410
- Accuracy: 0.9748
- Precision: 0.9751
- Recall: 0.9748
- F1: 0.9749
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2028 | 1.0 | 3180 | 0.2405 | 0.9535 | 0.9561 | 0.9535 | 0.9538 |
| 0.1632 | 2.0 | 6360 | 0.1686 | 0.9660 | 0.9664 | 0.9660 | 0.9661 |
| 0.1203 | 3.0 | 9540 | 0.1625 | 0.9648 | 0.9655 | 0.9648 | 0.9648 |
| 0.1233 | 4.0 | 12720 | 0.1510 | 0.9698 | 0.9702 | 0.9698 | 0.9699 |
| 0.0823 | 5.0 | 15900 | 0.1600 | 0.9730 | 0.9732 | 0.9730 | 0.9730 |
| 0.0453 | 6.0 | 19080 | 0.1953 | 0.9723 | 0.9724 | 0.9723 | 0.9723 |
| 0.031 | 7.0 | 22260 | 0.1754 | 0.9755 | 0.9755 | 0.9755 | 0.9755 |
| 0.0166 | 8.0 | 25440 | 0.2155 | 0.9739 | 0.9740 | 0.9739 | 0.9739 |
| 0.0036 | 9.0 | 28620 | 0.2519 | 0.9730 | 0.9733 | 0.9730 | 0.9730 |
| 0.0035 | 10.0 | 31800 | 0.2410 | 0.9748 | 0.9751 | 0.9748 | 0.9749 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1
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