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ACROSSAPPS_NDD-addressbook_test-content
lgk03/ACROSSAPPS_NDD-addressbook_test-content
ACROSSAPPS_NDD-addressbook_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
- Jul 20, 2024
- Updated
- Jul 20, 2024
- SHA
- fd4df1e5
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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. -->
ACROSSAPPS_NDD-addressbook_test-content
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: 0.1743
- Accuracy: 0.9066
- F1: 0.9097
- Precision: 0.9242
- Recall: 0.9066
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 |
|---|---|---|---|---|---|---|---|
| 0.0674 | 1.0 | 694 | 0.1589 | 0.9301 | 0.9318 | 0.9394 | 0.9301 |
| 0.0414 | 2.0 | 1388 | 0.1743 | 0.9066 | 0.9097 | 0.9242 | 0.9066 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jul 20, 2024
- Updated
- Jul 20, 2024