Instructions to use vaibhav9/hangman-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav9/hangman-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vaibhav9/hangman-bert-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vaibhav9/hangman-bert-large") model = AutoModelForMaskedLM.from_pretrained("vaibhav9/hangman-bert-large") - Notebooks
- Google Colab
- Kaggle
hangman-bert-large
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8323
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.8263 | 1.0 | 2609 | 1.8458 |
| 1.8227 | 2.0 | 5218 | 1.8441 |
| 1.8206 | 3.0 | 7827 | 1.8376 |
| 1.8146 | 4.0 | 10436 | 1.8349 |
| 1.8108 | 5.0 | 13045 | 1.8323 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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