---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: twitter-roberta-base-sentiment-latest
  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. -->

# twitter-roberta-base-sentiment-latest

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3725
- Accuracy: 0.798

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5281        | 0.1   | 50   | 0.5483          | 0.5845   |
| 0.4213        | 0.2   | 100  | 0.4663          | 0.671    |
| 0.4279        | 0.3   | 150  | 0.3972          | 0.7175   |
| 0.3765        | 0.4   | 200  | 0.3771          | 0.7425   |
| 0.3733        | 0.5   | 250  | 0.3884          | 0.755    |
| 0.4427        | 0.6   | 300  | 0.3535          | 0.7515   |
| 0.367         | 0.7   | 350  | 0.3511          | 0.765    |
| 0.3446        | 0.8   | 400  | 0.3422          | 0.7695   |
| 0.3339        | 0.9   | 450  | 0.3560          | 0.775    |
| 0.3681        | 1.0   | 500  | 0.3359          | 0.776    |
| 0.2586        | 1.1   | 550  | 0.3620          | 0.776    |
| 0.3399        | 1.2   | 600  | 0.3433          | 0.798    |
| 0.3881        | 1.3   | 650  | 0.3457          | 0.765    |
| 0.3443        | 1.4   | 700  | 0.3400          | 0.7885   |
| 0.2937        | 1.5   | 750  | 0.3475          | 0.7805   |
| 0.3363        | 1.6   | 800  | 0.3937          | 0.756    |
| 0.3363        | 1.7   | 850  | 0.3165          | 0.8065   |
| 0.3427        | 1.8   | 900  | 0.3374          | 0.7945   |
| 0.3457        | 1.9   | 950  | 0.3154          | 0.8055   |
| 0.3256        | 2.0   | 1000 | 0.3412          | 0.7945   |
| 0.1914        | 2.1   | 1050 | 0.3785          | 0.8005   |
| 0.1546        | 2.2   | 1100 | 0.3921          | 0.798    |
| 0.1931        | 2.3   | 1150 | 0.3766          | 0.797    |
| 0.2315        | 2.4   | 1200 | 0.3866          | 0.7985   |
| 0.1973        | 2.5   | 1250 | 0.3758          | 0.7975   |
| 0.3116        | 2.6   | 1300 | 0.3839          | 0.7975   |
| 0.245         | 2.7   | 1350 | 0.3770          | 0.7945   |
| 0.144         | 2.8   | 1400 | 0.3774          | 0.793    |
| 0.219         | 2.9   | 1450 | 0.3833          | 0.792    |
| 0.2341        | 3.0   | 1500 | 0.3725          | 0.798    |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1