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distilbert-base-uncased_fold_5_binary
elopezlopez/distilbert-base-uncased_fold_5_binary
distilbert-base-uncased_fold_5_binary 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
- Jul 31, 2022
- SHA
- aa619d69
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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
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: 0.5093
- F1: 0.7801
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 | 0.4760 | 0.7315 |
| 0.3992 | 2.0 | 576 | 0.4428 | 0.7785 |
| 0.3992 | 3.0 | 864 | 0.5093 | 0.7801 |
| 0.2021 | 4.0 | 1152 | 0.6588 | 0.7634 |
| 0.2021 | 5.0 | 1440 | 0.9174 | 0.7713 |
| 0.0945 | 6.0 | 1728 | 0.9832 | 0.7726 |
| 0.0321 | 7.0 | 2016 | 1.2103 | 0.7672 |
| 0.0321 | 8.0 | 2304 | 1.3759 | 0.7616 |
| 0.0134 | 9.0 | 2592 | 1.4405 | 0.7570 |
| 0.0134 | 10.0 | 2880 | 1.4591 | 0.7710 |
| 0.0117 | 11.0 | 3168 | 1.4947 | 0.7713 |
| 0.0117 | 12.0 | 3456 | 1.6224 | 0.7419 |
| 0.0081 | 13.0 | 3744 | 1.6462 | 0.7520 |
| 0.0083 | 14.0 | 4032 | 1.6880 | 0.7637 |
| 0.0083 | 15.0 | 4320 | 1.7080 | 0.7380 |
| 0.0048 | 16.0 | 4608 | 1.7352 | 0.7551 |
| 0.0048 | 17.0 | 4896 | 1.6761 | 0.7713 |
| 0.0024 | 18.0 | 5184 | 1.7553 | 0.76 |
| 0.0024 | 19.0 | 5472 | 1.7312 | 0.7673 |
| 0.005 | 20.0 | 5760 | 1.7334 | 0.7713 |
| 0.0032 | 21.0 | 6048 | 1.7963 | 0.7578 |
| 0.0032 | 22.0 | 6336 | 1.7529 | 0.7679 |
| 0.0025 | 23.0 | 6624 | 1.7741 | 0.7662 |
| 0.0025 | 24.0 | 6912 | 1.7515 | 0.7679 |
| 0.0004 | 25.0 | 7200 | 1.7370 | 0.7765 |
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
- Jul 31, 2022