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distilbert-base-uncased-finetuned-sst2
Gregorig/distilbert-base-uncased-finetuned-sst2
distilbert-base-uncased-finetuned-sst2 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
- Mar 5, 2024
- Updated
- Mar 5, 2024
- SHA
- 95f954e3
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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-sst2
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: 0.2910
- Accuracy: 0.8784
- Precision: 0.8784
- Recall: 0.8784
- F1: 0.8784
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4308 | 1.0 | 109 | 0.2942 | 0.875 | 0.8752 | 0.875 | 0.8750 |
| 0.2456 | 2.0 | 218 | 0.2910 | 0.8784 | 0.8784 | 0.8784 | 0.8784 |
Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu121
- Datasets 2.0.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 5, 2024
- Updated
- Mar 5, 2024