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without-cq
albarpambagio/without-cq
without-cq is a text classification model from albarpambagio. 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
- Jul 10, 2024
- Updated
- Jul 10, 2024
- SHA
- aabd0747
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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. -->
without-cq
This model is a fine-tuned version of indolem/indobertweet-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1785
- Accuracy: 0.6604
- Precision: 0.6734
- Recall: 0.6604
- F1: 0.6643
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0888 | 1.0 | 266 | 1.9147 | 0.6660 | 0.6707 | 0.6660 | 0.6668 |
| 0.0379 | 2.0 | 532 | 2.1358 | 0.6566 | 0.6523 | 0.6566 | 0.6525 |
| 0.0568 | 3.0 | 798 | 2.1785 | 0.6604 | 0.6734 | 0.6604 | 0.6643 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jul 10, 2024
- Updated
- Jul 10, 2024