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Vaccine-tweet-sentiments-analysis-model-2
Ausbel/Vaccine-tweet-sentiments-analysis-model-2
Vaccine-tweet-sentiments-analysis-model-2 is a text classification model from Ausbel. 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
- Jul 24, 2023
- Updated
- Jul 24, 2023
- SHA
- d5b122ec
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Base models
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. -->
test_trainer
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6861
- Accuracy: 0.714
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8144 | 0.5 | 500 | 0.7865 | 0.7165 |
| 0.7546 | 1.0 | 1000 | 0.7388 | 0.7045 |
| 0.7549 | 1.5 | 1500 | 0.7202 | 0.715 |
| 0.7124 | 2.0 | 2000 | 0.6834 | 0.7145 |
| 0.689 | 2.5 | 2500 | 0.7048 | 0.712 |
| 0.6924 | 3.0 | 3000 | 0.6861 | 0.714 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Jul 24, 2023
- Updated
- Jul 24, 2023