---
library_name: transformers
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-tweet-finalVersion
  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. -->

# finetuning-sentiment-model-tweet-finalVersion

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: 0.8178
- Precision Negative: 0.8125
- Recall Negative: 0.7222
- F1 Negative: 0.7647
- Precision Neutral: 0.8140
- Recall Neutral: 0.875
- F1 Neutral: 0.8434
- Precision Positive: 0.8889
- Recall Positive: 0.8571
- F1 Positive: 0.8727
- Accuracy: 0.8372
- Confusion Matrix: [[26, 9, 1], [5, 70, 5], [1, 7, 48]]

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision Negative | Recall Negative | F1 Negative | Precision Neutral | Recall Neutral | F1 Neutral | Precision Positive | Recall Positive | F1 Positive | Accuracy | Confusion Matrix                       |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:-----------------:|:--------------:|:----------:|:------------------:|:---------------:|:-----------:|:--------:|:--------------------------------------:|
| 0.496         | 1.0   | 22   | 0.7011          | 0.875              | 0.5833          | 0.7         | 0.7792            | 0.75           | 0.7643     | 0.7183             | 0.9107          | 0.8031      | 0.7674   | [[21, 12, 3], [3, 60, 17], [0, 5, 51]] |
| 0.3789        | 2.0   | 44   | 0.6227          | 0.725              | 0.8056          | 0.7632      | 0.7582            | 0.8625         | 0.8070     | 0.9756             | 0.7143          | 0.8247      | 0.8023   | [[29, 7, 0], [10, 69, 1], [1, 15, 40]] |
| 0.1735        | 3.0   | 66   | 0.6720          | 0.7879             | 0.7222          | 0.7536      | 0.8               | 0.85           | 0.8242     | 0.8704             | 0.8393          | 0.8545      | 0.8198   | [[26, 9, 1], [6, 68, 6], [1, 8, 47]]   |
| 0.1261        | 4.0   | 88   | 0.7001          | 0.8387             | 0.7222          | 0.7761      | 0.8046            | 0.875          | 0.8383     | 0.8704             | 0.8393          | 0.8545      | 0.8314   | [[26, 9, 1], [4, 70, 6], [1, 8, 47]]   |
| 0.0555        | 5.0   | 110  | 0.7969          | 0.8387             | 0.7222          | 0.7761      | 0.8140            | 0.875          | 0.8434     | 0.8727             | 0.8571          | 0.8649      | 0.8372   | [[26, 9, 1], [4, 70, 6], [1, 7, 48]]   |
| 0.035         | 6.0   | 132  | 0.8178          | 0.8125             | 0.7222          | 0.7647      | 0.8140            | 0.875          | 0.8434     | 0.8889             | 0.8571          | 0.8727      | 0.8372   | [[26, 9, 1], [5, 70, 5], [1, 7, 48]]   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3