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finetuned-bert-covid-tweets
Moniksama/finetuned-bert-covid-tweets
finetuned-bert-covid-tweets is a text classification model from Moniksama. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Apr 17, 2023
- Updated
- Jun 20, 2023
- SHA
- 11f747ad
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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. -->
finetuned-bert-covid-tweets
This model is a fine-tuned version of bert-base-uncased on the https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification dataset. It achieves the following results on the evaluation set:
- Loss: 0.2862
- Accuracy: 0.9175
- F1-score: 0.9177
- Precision: 0.9179
- Recall: 0.9175
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1911 | 1.0 | 513 | 0.2740 | 0.9176 | 0.9176 | 0.9185 | 0.9176 |
| 0.128 | 2.0 | 1026 | 0.2689 | 0.9262 | 0.9264 | 0.9267 | 0.9262 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 17, 2023
- Updated
- Jun 20, 2023