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topic-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_1wd
annabellehuether/topic-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_1wd
topic-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_1wd 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
- e6020c92
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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_1wd
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: 0.9095
- Accuracy: 0.7392
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 |
|---|---|---|---|---|
| 1.3067 | 1.0 | 660 | 0.9220 | 0.7103 |
| 0.8105 | 2.0 | 1320 | 0.8366 | 0.7384 |
| 0.6656 | 3.0 | 1980 | 0.8202 | 0.7425 |
| 0.4105 | 4.0 | 2640 | 0.8823 | 0.7384 |
| 0.3359 | 5.0 | 3300 | 0.9095 | 0.7392 |
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