Downloads · 30 days
7
18% of all-time downloads
ditilbert-spamEmail
tony4194/ditilbert-spamEmail
ditilbert-spamEmail is a text classification model from tony4194. 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. --
All-time downloads
38
Public
Repo size
804 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin268 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Oct 17, 2023
- Updated
- Oct 19, 2023
- SHA
- ea7bb7da
Try a prompt
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. -->
ditilbert-spamEmail
This model is a fine-tuned version of distilbert-base-uncased on an enron_spam dataset. It achieves the following results on the evaluation set:
- Loss: 0.0462
- Accuracy: 0.9925
Model description
By calling the API, label 0 means ham message while 1 means spam message.
Intended uses & limitations
This model is used for spam email detection powered by distilbert and sequence classification.
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0307 | 1.0 | 1983 | 0.0561 | 0.989 |
| 0.007 | 2.0 | 3966 | 0.0462 | 0.9925 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Oct 17, 2023
- Updated
- Oct 19, 2023