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distilbert-base-uncased_fold_9_ternary_v1
elopezlopez/distilbert-base-uncased_fold_9_ternary_v1
distilbert-base-uncased_fold_9_ternary_v1 is a text classification model from elopezlopez. 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
- Aug 2, 2022
- Updated
- Aug 2, 2022
- SHA
- 92560cbb
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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_fold_9_ternary_v1
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9406
- F1: 0.7841
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 292 | 0.5684 | 0.7635 |
| 0.5656 | 2.0 | 584 | 0.5753 | 0.7725 |
| 0.5656 | 3.0 | 876 | 0.6159 | 0.7866 |
| 0.2499 | 4.0 | 1168 | 0.7743 | 0.7828 |
| 0.2499 | 5.0 | 1460 | 0.9820 | 0.7674 |
| 0.1153 | 6.0 | 1752 | 1.2383 | 0.7738 |
| 0.0547 | 7.0 | 2044 | 1.2468 | 0.7815 |
| 0.0547 | 8.0 | 2336 | 1.3480 | 0.7622 |
| 0.0233 | 9.0 | 2628 | 1.3791 | 0.7892 |
| 0.0233 | 10.0 | 2920 | 1.4344 | 0.7841 |
| 0.0142 | 11.0 | 3212 | 1.4958 | 0.7802 |
| 0.0087 | 12.0 | 3504 | 1.5714 | 0.7674 |
| 0.0087 | 13.0 | 3796 | 1.6129 | 0.7956 |
| 0.0111 | 14.0 | 4088 | 1.7799 | 0.7751 |
| 0.0111 | 15.0 | 4380 | 1.7272 | 0.7789 |
| 0.0055 | 16.0 | 4672 | 1.7696 | 0.7866 |
| 0.0055 | 17.0 | 4964 | 1.8622 | 0.7789 |
| 0.003 | 18.0 | 5256 | 1.8563 | 0.7802 |
| 0.0004 | 19.0 | 5548 | 1.8993 | 0.7815 |
| 0.0004 | 20.0 | 5840 | 1.9199 | 0.7853 |
| 0.0005 | 21.0 | 6132 | 1.9003 | 0.7879 |
| 0.0005 | 22.0 | 6424 | 1.9161 | 0.7828 |
| 0.0011 | 23.0 | 6716 | 1.9691 | 0.7815 |
| 0.0017 | 24.0 | 7008 | 1.9492 | 0.7841 |
| 0.0017 | 25.0 | 7300 | 1.9406 | 0.7841 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Aug 2, 2022
- Updated
- Aug 2, 2022