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bert_base_train_book_ent_15p_ra
gokulsrinivasagan/bert_base_train_book_ent_15p_ra
bert_base_train_book_ent_15p_ra is a fill-mask model from gokulsrinivasagan. 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
- distilbert
- Access
- Public
- Created
- Apr 7, 2025
- Updated
- Apr 8, 2025
- SHA
- 5b6313db
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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_base_train_book_ent_15p_ra
This model is a fine-tuned version of distilbert-base-uncased on the gokulsrinivasagan/processed_wikitext-103-raw-v1-ld dataset. It achieves the following results on the evaluation set:
- Loss: 2.0460
- Accuracy: 0.6615
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: 0.0001
- train_batch_size: 176
- eval_batch_size: 176
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.8334 | 7.6923 | 10000 | 2.5770 | 0.5893 |
| 2.0208 | 15.3846 | 20000 | 1.9029 | 0.6768 |
| 1.8127 | 23.0769 | 30000 | 1.8306 | 0.6938 |
Framework versions
- Transformers 4.51.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
- Task
- Fill-Mask
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Apr 7, 2025
- Updated
- Apr 8, 2025