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distilbert-base-uncased_fold_4_ternary
elopezlopez/distilbert-base-uncased_fold_4_ternary
distilbert-base-uncased_fold_4_ternary 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
- Jul 31, 2022
- Updated
- Aug 1, 2022
- SHA
- 03b38db8
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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_4_ternary
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: 1.2981
- F1: 0.7565
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 | 289 | 0.5588 | 0.6984 |
| 0.5547 | 2.0 | 578 | 0.5283 | 0.7336 |
| 0.5547 | 3.0 | 867 | 0.7038 | 0.7202 |
| 0.2479 | 4.0 | 1156 | 0.8949 | 0.7284 |
| 0.2479 | 5.0 | 1445 | 0.9959 | 0.7286 |
| 0.1181 | 6.0 | 1734 | 1.0663 | 0.7311 |
| 0.0508 | 7.0 | 2023 | 1.2377 | 0.7054 |
| 0.0508 | 8.0 | 2312 | 1.2981 | 0.7565 |
| 0.0185 | 9.0 | 2601 | 1.3532 | 0.7407 |
| 0.0185 | 10.0 | 2890 | 1.5365 | 0.7333 |
| 0.0103 | 11.0 | 3179 | 1.5184 | 0.7423 |
| 0.0103 | 12.0 | 3468 | 1.6009 | 0.7420 |
| 0.0123 | 13.0 | 3757 | 1.6395 | 0.7402 |
| 0.008 | 14.0 | 4046 | 1.6838 | 0.7429 |
| 0.008 | 15.0 | 4335 | 1.6176 | 0.7490 |
| 0.0012 | 16.0 | 4624 | 1.7873 | 0.7345 |
| 0.0012 | 17.0 | 4913 | 1.6761 | 0.7412 |
| 0.0044 | 18.0 | 5202 | 1.7356 | 0.7417 |
| 0.0044 | 19.0 | 5491 | 1.7686 | 0.7502 |
| 0.0045 | 20.0 | 5780 | 1.7668 | 0.7406 |
| 0.0017 | 21.0 | 6069 | 1.8411 | 0.7381 |
| 0.0017 | 22.0 | 6358 | 1.8147 | 0.7469 |
| 0.0012 | 23.0 | 6647 | 1.8028 | 0.7489 |
| 0.0012 | 24.0 | 6936 | 1.8147 | 0.7453 |
| 0.0026 | 25.0 | 7225 | 1.8257 | 0.7475 |
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
- Jul 31, 2022
- Updated
- Aug 1, 2022