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distilbert-base-uncased_fold_5_binary_v1
elopezlopez/distilbert-base-uncased_fold_5_binary_v1
distilbert-base-uncased_fold_5_binary_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
- 74990f50
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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_5_binary_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: 1.6980
- F1: 0.8110
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 | 288 | 0.4412 | 0.7981 |
| 0.396 | 2.0 | 576 | 0.4419 | 0.8078 |
| 0.396 | 3.0 | 864 | 0.4955 | 0.8166 |
| 0.2019 | 4.0 | 1152 | 0.6341 | 0.8075 |
| 0.2019 | 5.0 | 1440 | 1.0351 | 0.7979 |
| 0.0808 | 6.0 | 1728 | 1.1818 | 0.7844 |
| 0.0315 | 7.0 | 2016 | 1.2530 | 0.8051 |
| 0.0315 | 8.0 | 2304 | 1.3568 | 0.7937 |
| 0.0143 | 9.0 | 2592 | 1.4009 | 0.8045 |
| 0.0143 | 10.0 | 2880 | 1.5333 | 0.7941 |
| 0.0066 | 11.0 | 3168 | 1.5242 | 0.7982 |
| 0.0066 | 12.0 | 3456 | 1.5752 | 0.8050 |
| 0.0091 | 13.0 | 3744 | 1.5199 | 0.8046 |
| 0.0111 | 14.0 | 4032 | 1.5319 | 0.8117 |
| 0.0111 | 15.0 | 4320 | 1.5333 | 0.8156 |
| 0.0072 | 16.0 | 4608 | 1.5461 | 0.8192 |
| 0.0072 | 17.0 | 4896 | 1.5288 | 0.8252 |
| 0.0048 | 18.0 | 5184 | 1.5725 | 0.8078 |
| 0.0048 | 19.0 | 5472 | 1.5896 | 0.8138 |
| 0.0032 | 20.0 | 5760 | 1.6917 | 0.8071 |
| 0.0028 | 21.0 | 6048 | 1.6608 | 0.8109 |
| 0.0028 | 22.0 | 6336 | 1.7013 | 0.8122 |
| 0.0029 | 23.0 | 6624 | 1.6769 | 0.8148 |
| 0.0029 | 24.0 | 6912 | 1.6906 | 0.8100 |
| 0.0006 | 25.0 | 7200 | 1.6980 | 0.8110 |
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