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topic-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd
annabellehuether/topic-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd
topic-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd is a text classification model from annabellehuether. 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
- bert
- Access
- Public
- Created
- Dec 4, 2023
- Updated
- Dec 4, 2023
- SHA
- 7f7a4145
Try a prompt
Base models
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. -->
topic-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9049
- Accuracy: 0.7414
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: 32
- eval_batch_size: 32
- seed: 7
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3078 | 1.0 | 660 | 0.9307 | 0.7073 |
| 0.811 | 2.0 | 1320 | 0.8368 | 0.7429 |
| 0.6684 | 3.0 | 1980 | 0.8197 | 0.7406 |
| 0.4163 | 4.0 | 2640 | 0.8724 | 0.7443 |
| 0.34 | 5.0 | 3300 | 0.9049 | 0.7414 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 4, 2023
- Updated
- Dec 4, 2023