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bert-base-uncased-finetuned-cda
zz990906/bert-base-uncased-finetuned-cda
bert-base-uncased-finetuned-cda is a fill-mask model from zz990906. Use it when you need the model to fill a missing word. 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
- Fill-Mask
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Nov 10, 2023
- Updated
- Nov 10, 2023
- SHA
- 6b2ff54b
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. -->
bert-base-uncased-finetuned-cda
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: 1.6567
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0518 | 1.0 | 391 | 1.8074 |
| 1.8971 | 2.0 | 782 | 1.7770 |
| 1.8422 | 3.0 | 1173 | 1.7504 |
| 1.7984 | 4.0 | 1564 | 1.7272 |
| 1.777 | 5.0 | 1955 | 1.6912 |
| 1.7532 | 6.0 | 2346 | 1.6920 |
| 1.7323 | 7.0 | 2737 | 1.6826 |
| 1.7251 | 8.0 | 3128 | 1.6687 |
| 1.7108 | 9.0 | 3519 | 1.6553 |
| 1.7076 | 10.0 | 3910 | 1.6702 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Fill-Mask
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 10, 2023
- Updated
- Nov 10, 2023