---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023
  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. -->

# cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023

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: 0.3189
- Accuracy: 0.805

## 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: 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6619        | 0.2   | 100  | 0.5226          | 0.6285   |
| 0.4526        | 0.4   | 200  | 0.4150          | 0.716    |
| 0.4092        | 0.6   | 300  | 0.3898          | 0.728    |
| 0.3886        | 0.8   | 400  | 0.3441          | 0.773    |
| 0.3822        | 1.0   | 500  | 0.3494          | 0.767    |
| 0.3396        | 1.2   | 600  | 0.3470          | 0.7865   |
| 0.3156        | 1.4   | 700  | 0.3418          | 0.7875   |
| 0.3099        | 1.6   | 800  | 0.3231          | 0.794    |
| 0.2994        | 1.8   | 900  | 0.3371          | 0.7885   |
| 0.2907        | 2.0   | 1000 | 0.3189          | 0.805    |


### Framework versions

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