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distilbert-base-uncased_classification_finetuned_ptt_adptive
Mou11209203/distilbert-base-uncased_classification_finetuned_ptt_adptive
distilbert-base-uncased_classification_finetuned_ptt_adptive is a text classification model from Mou11209203. 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
- Apr 13, 2024
- Updated
- Apr 13, 2024
- SHA
- 5c31d386
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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_classification_finetuned_ptt_adptive
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6438
- F1: 0.5210
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: 3e-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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.6317 | 1.0 | 260 | 0.6438 | 0.5210 |
| 0.6565 | 2.0 | 520 | 0.6460 | 0.5210 |
| 0.6678 | 3.0 | 780 | 0.6434 | 0.5210 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Apr 13, 2024
- Updated
- Apr 13, 2024