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distilbert-base-uncased_fold_4_binary
elopezlopez/distilbert-base-uncased_fold_4_binary
distilbert-base-uncased_fold_4_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
- 5b0c3fe9
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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_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: 1.2977
- F1: 0.8083
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 | 0.3701 | 0.7903 |
| 0.4005 | 2.0 | 578 | 0.3669 | 0.7994 |
| 0.4005 | 3.0 | 867 | 0.5038 | 0.7955 |
| 0.1945 | 4.0 | 1156 | 0.6353 | 0.8006 |
| 0.1945 | 5.0 | 1445 | 0.8974 | 0.7826 |
| 0.0909 | 6.0 | 1734 | 0.8533 | 0.7764 |
| 0.0389 | 7.0 | 2023 | 0.9969 | 0.7957 |
| 0.0389 | 8.0 | 2312 | 1.0356 | 0.7952 |
| 0.0231 | 9.0 | 2601 | 1.1538 | 0.7963 |
| 0.0231 | 10.0 | 2890 | 1.2011 | 0.7968 |
| 0.0051 | 11.0 | 3179 | 1.2329 | 0.7935 |
| 0.0051 | 12.0 | 3468 | 1.2829 | 0.8056 |
| 0.0066 | 13.0 | 3757 | 1.2946 | 0.7956 |
| 0.004 | 14.0 | 4046 | 1.2977 | 0.8083 |
| 0.004 | 15.0 | 4335 | 1.3970 | 0.7957 |
| 0.0007 | 16.0 | 4624 | 1.3361 | 0.7917 |
| 0.0007 | 17.0 | 4913 | 1.5782 | 0.7954 |
| 0.0107 | 18.0 | 5202 | 1.4641 | 0.7900 |
| 0.0107 | 19.0 | 5491 | 1.4490 | 0.7957 |
| 0.0058 | 20.0 | 5780 | 1.4607 | 0.7932 |
| 0.0016 | 21.0 | 6069 | 1.5048 | 0.7939 |
| 0.0016 | 22.0 | 6358 | 1.5219 | 0.7945 |
| 0.0027 | 23.0 | 6647 | 1.4783 | 0.7937 |
| 0.0027 | 24.0 | 6936 | 1.4715 | 0.7981 |
| 0.0004 | 25.0 | 7225 | 1.4989 | 0.7900 |
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