Instructions to use ncduy/bert-base-cased-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncduy/bert-base-cased-wikitext2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ncduy/bert-base-cased-wikitext2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ncduy/bert-base-cased-wikitext2") model = AutoModelForMaskedLM.from_pretrained("ncduy/bert-base-cased-wikitext2") - Notebooks
- Google Colab
- Kaggle
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bert-base-cased-wikitext2
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.8565
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.0916 | 1.0 | 2346 | 7.0492 |
| 6.9074 | 2.0 | 4692 | 6.8727 |
| 6.8588 | 3.0 | 7038 | 6.8914 |
Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
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