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---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 20240320102435_big_hinton
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. -->
# 20240320102435_big_hinton
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0351
- Precision: 0.9436
- Recall: 0.9308
- F1: 0.9372
- Accuracy: 0.9859
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 350
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0805 | 0.09 | 300 | 0.0626 | 0.9020 | 0.8843 | 0.8931 | 0.9758 |
| 0.0969 | 0.18 | 600 | 0.0770 | 0.8912 | 0.8486 | 0.8694 | 0.9704 |
| 0.0879 | 0.27 | 900 | 0.0682 | 0.8943 | 0.8733 | 0.8837 | 0.9735 |
| 0.0778 | 0.36 | 1200 | 0.0612 | 0.9013 | 0.8891 | 0.8952 | 0.9762 |
| 0.0703 | 0.44 | 1500 | 0.0564 | 0.9137 | 0.8909 | 0.9021 | 0.9779 |
| 0.0638 | 0.53 | 1800 | 0.0521 | 0.9244 | 0.8975 | 0.9107 | 0.9799 |
| 0.0579 | 0.62 | 2100 | 0.0480 | 0.9309 | 0.9029 | 0.9167 | 0.9812 |
| 0.0534 | 0.71 | 2400 | 0.0447 | 0.9323 | 0.9095 | 0.9208 | 0.9825 |
| 0.049 | 0.8 | 2700 | 0.0399 | 0.9329 | 0.9236 | 0.9282 | 0.9841 |
| 0.0451 | 0.89 | 3000 | 0.0373 | 0.9411 | 0.9226 | 0.9318 | 0.9849 |
| 0.0424 | 0.98 | 3300 | 0.0351 | 0.9436 | 0.9308 | 0.9372 | 0.9859 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0a0+6a974be
- Datasets 2.18.0
- Tokenizers 0.15.2
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