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hw2model
coolseyungerm/hw2model
hw2model is a text classification model from coolseyungerm. 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
- Mar 1, 2024
- Updated
- Mar 1, 2024
- SHA
- 7c0f022d
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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. -->
hw2model
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: 0.4425
- Precision: {'precision': 0.7715038708614725}
- Recall: {'recall': 0.7542626491155903}
- F1: {'f1': 0.7613370975651783}
- Accuracy: {'accuracy': 0.7945619335347432}
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4599 | 1.0 | 1324 | 0.4425 | {'precision': 0.7715038708614725} | {'recall': 0.7542626491155903} | {'f1': 0.7613370975651783} | {'accuracy': 0.7945619335347432} |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 1, 2024
- Updated
- Mar 1, 2024