---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-sst-2-16-13-smoothed
  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-sst-2-16-13-smoothed

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6686
- Accuracy: 0.6875

## 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: 1e-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
- lr_scheduler_warmup_steps: 50
- num_epochs: 75
- label_smoothing_factor: 0.45

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 0.7007          | 0.5      |
| No log        | 2.0   | 2    | 0.7007          | 0.5      |
| No log        | 3.0   | 3    | 0.7005          | 0.5      |
| No log        | 4.0   | 4    | 0.7004          | 0.5      |
| No log        | 5.0   | 5    | 0.7001          | 0.5      |
| No log        | 6.0   | 6    | 0.6998          | 0.5      |
| No log        | 7.0   | 7    | 0.6994          | 0.5      |
| No log        | 8.0   | 8    | 0.6990          | 0.5      |
| No log        | 9.0   | 9    | 0.6986          | 0.5      |
| 0.6996        | 10.0  | 10   | 0.6982          | 0.5      |
| 0.6996        | 11.0  | 11   | 0.6977          | 0.5      |
| 0.6996        | 12.0  | 12   | 0.6972          | 0.5      |
| 0.6996        | 13.0  | 13   | 0.6967          | 0.5312   |
| 0.6996        | 14.0  | 14   | 0.6962          | 0.5312   |
| 0.6996        | 15.0  | 15   | 0.6956          | 0.5      |
| 0.6996        | 16.0  | 16   | 0.6950          | 0.5312   |
| 0.6996        | 17.0  | 17   | 0.6944          | 0.5625   |
| 0.6996        | 18.0  | 18   | 0.6939          | 0.625    |
| 0.6996        | 19.0  | 19   | 0.6935          | 0.5938   |
| 0.6877        | 20.0  | 20   | 0.6930          | 0.5625   |
| 0.6877        | 21.0  | 21   | 0.6926          | 0.5625   |
| 0.6877        | 22.0  | 22   | 0.6920          | 0.5938   |
| 0.6877        | 23.0  | 23   | 0.6915          | 0.5938   |
| 0.6877        | 24.0  | 24   | 0.6909          | 0.5938   |
| 0.6877        | 25.0  | 25   | 0.6903          | 0.5938   |
| 0.6877        | 26.0  | 26   | 0.6897          | 0.5625   |
| 0.6877        | 27.0  | 27   | 0.6892          | 0.5625   |
| 0.6877        | 28.0  | 28   | 0.6888          | 0.5625   |
| 0.6877        | 29.0  | 29   | 0.6880          | 0.5625   |
| 0.6693        | 30.0  | 30   | 0.6873          | 0.5625   |
| 0.6693        | 31.0  | 31   | 0.6872          | 0.5625   |
| 0.6693        | 32.0  | 32   | 0.6869          | 0.5938   |
| 0.6693        | 33.0  | 33   | 0.6863          | 0.5625   |
| 0.6693        | 34.0  | 34   | 0.6857          | 0.5625   |
| 0.6693        | 35.0  | 35   | 0.6851          | 0.5625   |
| 0.6693        | 36.0  | 36   | 0.6844          | 0.5938   |
| 0.6693        | 37.0  | 37   | 0.6836          | 0.5938   |
| 0.6693        | 38.0  | 38   | 0.6825          | 0.5938   |
| 0.6693        | 39.0  | 39   | 0.6818          | 0.5625   |
| 0.6173        | 40.0  | 40   | 0.6815          | 0.5625   |
| 0.6173        | 41.0  | 41   | 0.6816          | 0.5625   |
| 0.6173        | 42.0  | 42   | 0.6812          | 0.5938   |
| 0.6173        | 43.0  | 43   | 0.6804          | 0.625    |
| 0.6173        | 44.0  | 44   | 0.6794          | 0.6562   |
| 0.6173        | 45.0  | 45   | 0.6781          | 0.6562   |
| 0.6173        | 46.0  | 46   | 0.6770          | 0.6562   |
| 0.6173        | 47.0  | 47   | 0.6761          | 0.6562   |
| 0.6173        | 48.0  | 48   | 0.6757          | 0.6562   |
| 0.6173        | 49.0  | 49   | 0.6746          | 0.6562   |
| 0.5541        | 50.0  | 50   | 0.6736          | 0.6562   |
| 0.5541        | 51.0  | 51   | 0.6730          | 0.6562   |
| 0.5541        | 52.0  | 52   | 0.6725          | 0.6562   |
| 0.5541        | 53.0  | 53   | 0.6703          | 0.6562   |
| 0.5541        | 54.0  | 54   | 0.6682          | 0.6562   |
| 0.5541        | 55.0  | 55   | 0.6671          | 0.6562   |
| 0.5541        | 56.0  | 56   | 0.6660          | 0.6562   |
| 0.5541        | 57.0  | 57   | 0.6652          | 0.6562   |
| 0.5541        | 58.0  | 58   | 0.6651          | 0.6562   |
| 0.5541        | 59.0  | 59   | 0.6652          | 0.6562   |
| 0.5403        | 60.0  | 60   | 0.6654          | 0.6875   |
| 0.5403        | 61.0  | 61   | 0.6660          | 0.6562   |
| 0.5403        | 62.0  | 62   | 0.6664          | 0.6562   |
| 0.5403        | 63.0  | 63   | 0.6666          | 0.6562   |
| 0.5403        | 64.0  | 64   | 0.6675          | 0.6562   |
| 0.5403        | 65.0  | 65   | 0.6684          | 0.6562   |
| 0.5403        | 66.0  | 66   | 0.6692          | 0.6562   |
| 0.5403        | 67.0  | 67   | 0.6699          | 0.6562   |
| 0.5403        | 68.0  | 68   | 0.6703          | 0.6562   |
| 0.5403        | 69.0  | 69   | 0.6705          | 0.6562   |
| 0.538         | 70.0  | 70   | 0.6706          | 0.6562   |
| 0.538         | 71.0  | 71   | 0.6703          | 0.6562   |
| 0.538         | 72.0  | 72   | 0.6696          | 0.6562   |
| 0.538         | 73.0  | 73   | 0.6691          | 0.6562   |
| 0.538         | 74.0  | 74   | 0.6688          | 0.6562   |
| 0.538         | 75.0  | 75   | 0.6686          | 0.6875   |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3