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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
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
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: 0.2457
- Precision: 0.9489
- Recall: 0.9480
- F1: 0.9485
- Accuracy: 0.9405
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.4723 | 0.8973 | 0.9212 | 0.9091 | 0.8971 |
| No log | 2.0 | 60 | 0.3328 | 0.9146 | 0.9288 | 0.9217 | 0.9076 |
| No log | 3.0 | 90 | 0.3022 | 0.9316 | 0.9301 | 0.9308 | 0.9168 |
| No log | 4.0 | 120 | 0.2758 | 0.9207 | 0.9398 | 0.9301 | 0.9169 |
| No log | 5.0 | 150 | 0.2592 | 0.9392 | 0.9431 | 0.9411 | 0.9322 |
| No log | 6.0 | 180 | 0.2586 | 0.9445 | 0.9449 | 0.9447 | 0.9366 |
| No log | 7.0 | 210 | 0.2519 | 0.9476 | 0.9447 | 0.9461 | 0.9372 |
| No log | 8.0 | 240 | 0.2468 | 0.9464 | 0.9474 | 0.9469 | 0.9394 |
| No log | 9.0 | 270 | 0.2475 | 0.9486 | 0.9476 | 0.9481 | 0.9399 |
| No log | 10.0 | 300 | 0.2457 | 0.9489 | 0.9480 | 0.9485 | 0.9405 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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