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clasificador-Spam
CarlaLlavador/clasificador-Spam
clasificador-Spam is a text classification model from CarlaLlavador. 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
- distilbert
- Access
- Public
- Created
- Jan 4, 2026
- Updated
- Jan 4, 2026
- SHA
- c2a79c79
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Base models
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. -->
clasificador-Spam
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the ucirvine/sms_spam dataset. It achieves the following results on the evaluation set:
- Loss: 0.0272
- Accuracy: 0.9936
Model description
This model is trained whith a dataset of sms (ucirvine/sms_spam) to classify texts (sms or similar) between ham (0) and spam (1).
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 30 | 0.0374 | 0.9894 |
| No log | 2.0 | 60 | 0.0238 | 0.9915 |
| No log | 3.0 | 90 | 0.0272 | 0.9936 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cpu
- Datasets 4.4.2
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jan 4, 2026
- Updated
- Jan 4, 2026