---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-stationary-update
  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-uncased-finetuned-stationary-update

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8082
- Accuracy: 0.7967
- F1: 0.7872

## 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: 64
- eval_batch_size: 64
- 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 | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5673        | 1.0   | 38   | 0.5049          | 0.7667   | 0.7453 |
| 0.4018        | 2.0   | 76   | 0.4605          | 0.79     | 0.7853 |
| 0.3074        | 3.0   | 114  | 0.4991          | 0.7967   | 0.7941 |
| 0.2065        | 4.0   | 152  | 0.5517          | 0.7967   | 0.7914 |
| 0.1347        | 5.0   | 190  | 0.7082          | 0.7833   | 0.7655 |
| 0.1008        | 6.0   | 228  | 0.7469          | 0.7967   | 0.7811 |
| 0.0799        | 7.0   | 266  | 0.7609          | 0.7933   | 0.7823 |
| 0.0558        | 8.0   | 304  | 0.8108          | 0.7967   | 0.7853 |
| 0.0526        | 9.0   | 342  | 0.7988          | 0.79     | 0.7821 |
| 0.0426        | 10.0  | 380  | 0.8082          | 0.7967   | 0.7872 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0