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WITHINAPPS_NDD-ppma_test-content
lgk03/WITHINAPPS_NDD-ppma_test-content
WITHINAPPS_NDD-ppma_test-content is a text classification model from lgk03. 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
- May 29, 2024
- Updated
- May 29, 2024
- SHA
- cf39103f
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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. -->
WITHINAPPS_NDD-ppma_test-content
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2237
- Accuracy: 0.8795
- F1: 0.8231
- Precision: 0.7735
- Recall: 0.8795
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 0.9836 | 30 | 0.2373 | 0.8795 | 0.8231 | 0.7735 | 0.8795 |
| No log | 1.9672 | 60 | 0.2237 | 0.8795 | 0.8231 | 0.7735 | 0.8795 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 29, 2024
- Updated
- May 29, 2024