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distilbert_rand_5_v1_wnli
Hartunka/distilbert_rand_5_v1_wnli
distilbert_rand_5_v1_wnli is a text classification model from Hartunka. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- Apr 17, 2025
- Updated
- Apr 17, 2025
- SHA
- b410f8bf
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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_rand_5_v1_wnli
This model is a fine-tuned version of Hartunka/distilbert_rand_5_v1 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.7200
- Accuracy: 0.3944
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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7118 | 1.0 | 3 | 0.7200 | 0.3944 |
| 0.7057 | 2.0 | 6 | 0.7357 | 0.4225 |
| 0.7044 | 3.0 | 9 | 0.7693 | 0.3662 |
| 0.6962 | 4.0 | 12 | 0.7462 | 0.4789 |
| 0.6997 | 5.0 | 15 | 0.7560 | 0.3239 |
| 0.6895 | 6.0 | 18 | 0.7849 | 0.3099 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Languages
- en
- Created
- Apr 17, 2025
- Updated
- Apr 17, 2025