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bert-large-uncased-detect-dep-v5
Trong-Nghia/bert-large-uncased-detect-dep-v5
bert-large-uncased-detect-dep-v5 is a text classification model from Trong-Nghia. 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
- Jul 23, 2023
- Updated
- Jul 24, 2023
- SHA
- 21e817fa
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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. -->
bert-large-uncased-detect-dep-v5
This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6567
- Accuracy: 0.735
- F1: 0.8030
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6079 | 1.0 | 1502 | 0.5489 | 0.72 | 0.7764 |
| 0.5703 | 2.0 | 3004 | 0.5609 | 0.74 | 0.8110 |
| 0.5169 | 3.0 | 4506 | 0.6567 | 0.735 | 0.8030 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jul 23, 2023
- Updated
- Jul 24, 2023