| --- |
| 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 |
| |
