---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-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. -->

# roberta-large-sst-2-16-13-smoothed

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6487
- Accuracy: 0.75

## 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    | 0.7106          | 0.5      |
| No log        | 2.0   | 2    | 0.7104          | 0.5      |
| No log        | 3.0   | 3    | 0.7100          | 0.5      |
| No log        | 4.0   | 4    | 0.7094          | 0.5      |
| No log        | 5.0   | 5    | 0.7087          | 0.5      |
| No log        | 6.0   | 6    | 0.7077          | 0.5      |
| No log        | 7.0   | 7    | 0.7066          | 0.5      |
| No log        | 8.0   | 8    | 0.7054          | 0.5      |
| No log        | 9.0   | 9    | 0.7040          | 0.5      |
| 0.7172        | 10.0  | 10   | 0.7026          | 0.5      |
| 0.7172        | 11.0  | 11   | 0.7011          | 0.5      |
| 0.7172        | 12.0  | 12   | 0.6995          | 0.5      |
| 0.7172        | 13.0  | 13   | 0.6980          | 0.5      |
| 0.7172        | 14.0  | 14   | 0.6965          | 0.5312   |
| 0.7172        | 15.0  | 15   | 0.6951          | 0.5312   |
| 0.7172        | 16.0  | 16   | 0.6936          | 0.5312   |
| 0.7172        | 17.0  | 17   | 0.6921          | 0.5312   |
| 0.7172        | 18.0  | 18   | 0.6906          | 0.5312   |
| 0.7172        | 19.0  | 19   | 0.6895          | 0.5312   |
| 0.6997        | 20.0  | 20   | 0.6884          | 0.5312   |
| 0.6997        | 21.0  | 21   | 0.6874          | 0.5312   |
| 0.6997        | 22.0  | 22   | 0.6867          | 0.5625   |
| 0.6997        | 23.0  | 23   | 0.6860          | 0.5312   |
| 0.6997        | 24.0  | 24   | 0.6854          | 0.5938   |
| 0.6997        | 25.0  | 25   | 0.6846          | 0.6562   |
| 0.6997        | 26.0  | 26   | 0.6840          | 0.625    |
| 0.6997        | 27.0  | 27   | 0.6832          | 0.6562   |
| 0.6997        | 28.0  | 28   | 0.6826          | 0.6875   |
| 0.6997        | 29.0  | 29   | 0.6815          | 0.6875   |
| 0.6874        | 30.0  | 30   | 0.6804          | 0.6875   |
| 0.6874        | 31.0  | 31   | 0.6790          | 0.6875   |
| 0.6874        | 32.0  | 32   | 0.6772          | 0.6875   |
| 0.6874        | 33.0  | 33   | 0.6762          | 0.6562   |
| 0.6874        | 34.0  | 34   | 0.6753          | 0.6562   |
| 0.6874        | 35.0  | 35   | 0.6738          | 0.6875   |
| 0.6874        | 36.0  | 36   | 0.6725          | 0.6875   |
| 0.6874        | 37.0  | 37   | 0.6696          | 0.6875   |
| 0.6874        | 38.0  | 38   | 0.6687          | 0.6875   |
| 0.6874        | 39.0  | 39   | 0.6665          | 0.6875   |
| 0.6594        | 40.0  | 40   | 0.6643          | 0.6875   |
| 0.6594        | 41.0  | 41   | 0.6674          | 0.6875   |
| 0.6594        | 42.0  | 42   | 0.6733          | 0.6875   |
| 0.6594        | 43.0  | 43   | 0.6804          | 0.6875   |
| 0.6594        | 44.0  | 44   | 0.6731          | 0.6875   |
| 0.6594        | 45.0  | 45   | 0.6701          | 0.6875   |
| 0.6594        | 46.0  | 46   | 0.6687          | 0.6875   |
| 0.6594        | 47.0  | 47   | 0.6687          | 0.6562   |
| 0.6594        | 48.0  | 48   | 0.6757          | 0.625    |
| 0.6594        | 49.0  | 49   | 0.6739          | 0.6875   |
| 0.6089        | 50.0  | 50   | 0.6766          | 0.6875   |
| 0.6089        | 51.0  | 51   | 0.6724          | 0.6875   |
| 0.6089        | 52.0  | 52   | 0.6662          | 0.6875   |
| 0.6089        | 53.0  | 53   | 0.6664          | 0.6875   |
| 0.6089        | 54.0  | 54   | 0.6602          | 0.6875   |
| 0.6089        | 55.0  | 55   | 0.6505          | 0.6875   |
| 0.6089        | 56.0  | 56   | 0.6468          | 0.75     |
| 0.6089        | 57.0  | 57   | 0.6370          | 0.75     |
| 0.6089        | 58.0  | 58   | 0.6285          | 0.7812   |
| 0.6089        | 59.0  | 59   | 0.6267          | 0.7812   |
| 0.5694        | 60.0  | 60   | 0.6279          | 0.7812   |
| 0.5694        | 61.0  | 61   | 0.6364          | 0.7812   |
| 0.5694        | 62.0  | 62   | 0.6443          | 0.75     |
| 0.5694        | 63.0  | 63   | 0.6518          | 0.7812   |
| 0.5694        | 64.0  | 64   | 0.6634          | 0.7188   |
| 0.5694        | 65.0  | 65   | 0.6647          | 0.7188   |
| 0.5694        | 66.0  | 66   | 0.6679          | 0.7188   |
| 0.5694        | 67.0  | 67   | 0.6669          | 0.7188   |
| 0.5694        | 68.0  | 68   | 0.6626          | 0.7188   |
| 0.5694        | 69.0  | 69   | 0.6624          | 0.75     |
| 0.5618        | 70.0  | 70   | 0.6614          | 0.7188   |
| 0.5618        | 71.0  | 71   | 0.6592          | 0.75     |
| 0.5618        | 72.0  | 72   | 0.6571          | 0.75     |
| 0.5618        | 73.0  | 73   | 0.6541          | 0.75     |
| 0.5618        | 74.0  | 74   | 0.6499          | 0.75     |
| 0.5618        | 75.0  | 75   | 0.6487          | 0.75     |


### Framework versions

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