Instructions to use Nikojamesnow/bert-base-cased-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nikojamesnow/bert-base-cased-wikitext2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Nikojamesnow/bert-base-cased-wikitext2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Nikojamesnow/bert-base-cased-wikitext2") model = AutoModelForMaskedLM.from_pretrained("Nikojamesnow/bert-base-cased-wikitext2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-base-cased-wikitext2
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.8704
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 |
|---|---|---|---|
| 7.0988 | 1.0 | 2346 | 7.0574 |
| 6.9095 | 2.0 | 4692 | 6.8951 |
| 6.8601 | 3.0 | 7038 | 6.8773 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for Nikojamesnow/bert-base-cased-wikitext2
Base model
google-bert/bert-base-cased