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distilbert-base-uncased_fold_10_ternary_v1
elopezlopez/distilbert-base-uncased_fold_10_ternary_v1
distilbert-base-uncased_fold_10_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
- f31e7f13
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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_10_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: 1.9887
- F1: 0.7797
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 | 290 | 0.5701 | 0.7463 |
| 0.5651 | 2.0 | 580 | 0.5359 | 0.7748 |
| 0.5651 | 3.0 | 870 | 0.6043 | 0.7847 |
| 0.2605 | 4.0 | 1160 | 1.0124 | 0.7587 |
| 0.2605 | 5.0 | 1450 | 1.1140 | 0.7599 |
| 0.1223 | 6.0 | 1740 | 1.2713 | 0.7859 |
| 0.0469 | 7.0 | 2030 | 1.3188 | 0.7822 |
| 0.0469 | 8.0 | 2320 | 1.3819 | 0.7946 |
| 0.0279 | 9.0 | 2610 | 1.5444 | 0.7847 |
| 0.0279 | 10.0 | 2900 | 1.5851 | 0.7908 |
| 0.0084 | 11.0 | 3190 | 1.7003 | 0.7822 |
| 0.0084 | 12.0 | 3480 | 1.8148 | 0.7748 |
| 0.007 | 13.0 | 3770 | 1.7651 | 0.7748 |
| 0.008 | 14.0 | 4060 | 1.8423 | 0.7748 |
| 0.008 | 15.0 | 4350 | 1.7871 | 0.7809 |
| 0.0054 | 16.0 | 4640 | 1.9324 | 0.7748 |
| 0.0054 | 17.0 | 4930 | 1.8685 | 0.7809 |
| 0.0048 | 18.0 | 5220 | 1.9901 | 0.7797 |
| 0.002 | 19.0 | 5510 | 1.9273 | 0.7785 |
| 0.002 | 20.0 | 5800 | 1.9945 | 0.7809 |
| 0.0018 | 21.0 | 6090 | 1.9250 | 0.7785 |
| 0.0018 | 22.0 | 6380 | 1.9929 | 0.7822 |
| 0.0032 | 23.0 | 6670 | 1.9306 | 0.7859 |
| 0.0032 | 24.0 | 6960 | 1.9603 | 0.7847 |
| 0.0029 | 25.0 | 7250 | 1.9887 | 0.7797 |
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