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distilbert-base-uncased-finetuned-t_shipping
Gregorig/distilbert-base-uncased-finetuned-t_shipping
distilbert-base-uncased-finetuned-t_shipping is a text classification model from Gregorig. 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
- Jun 5, 2024
- Updated
- Jun 5, 2024
- SHA
- da74a888
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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. -->
distilbert-base-uncased-finetuned-t_shipping
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2413
- Accuracy: 0.91
- F1: 0.9099
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6146 | 1.0 | 26 | 0.5353 | 0.77 | 0.7692 |
| 0.4328 | 2.0 | 52 | 0.3545 | 0.89 | 0.8900 |
| 0.2347 | 3.0 | 78 | 0.2535 | 0.93 | 0.9300 |
| 0.168 | 4.0 | 104 | 0.2459 | 0.93 | 0.9300 |
| 0.1312 | 5.0 | 130 | 0.2413 | 0.91 | 0.9099 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jun 5, 2024
- Updated
- Jun 5, 2024