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roberta-sentiment-analysis-finetune
siberett/roberta-sentiment-analysis-finetune
roberta-sentiment-analysis-finetune is a text classification model from siberett. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Dec 28, 2022
- Updated
- Dec 28, 2022
- SHA
- 0245a09a
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Datasets
Model card
From the Hugging Face model README
<!-- 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-sentiment-analysis-finetune
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 0.8863
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: 64
- eval_batch_size: 64
- 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5451 | 1.0 | 713 | 0.5422 |
| 0.4785 | 2.0 | 1426 | 0.5585 |
| 0.4199 | 3.0 | 2139 | 0.5785 |
| 0.3608 | 4.0 | 2852 | 0.6038 |
| 0.3117 | 5.0 | 3565 | 0.6713 |
| 0.2684 | 6.0 | 4278 | 0.7366 |
| 0.2403 | 7.0 | 4991 | 0.7737 |
| 0.2137 | 8.0 | 5704 | 0.8276 |
| 0.1926 | 9.0 | 6417 | 0.8597 |
| 0.1778 | 10.0 | 7130 | 0.8863 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Dec 28, 2022
- Updated
- Dec 28, 2022