Instructions to use dwmit/ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwmit/ja with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dwmit/ja")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dwmit/ja") model = AutoModelForMaskedLM.from_pretrained("dwmit/ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: ja | |
| results: [] | |
| <!-- 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. --> | |
| # ja | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9466 | |
| ## 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: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | No log | 1.0 | 1326 | 1.2739 | | |
| | No log | 2.0 | 2652 | 1.1404 | | |
| | No log | 3.0 | 3978 | 1.0791 | | |
| | No log | 4.0 | 5304 | 1.0383 | | |
| | No log | 5.0 | 6630 | 1.0098 | | |
| | No log | 6.0 | 7956 | 0.9905 | | |
| | No log | 7.0 | 9282 | 0.9690 | | |
| | 1.1782 | 8.0 | 10608 | 0.9614 | | |
| | 1.1782 | 9.0 | 11934 | 0.9519 | | |
| | 1.1782 | 10.0 | 13260 | 0.9464 | | |
| ### Framework versions | |
| - Transformers 4.28.1 | |
| - Pytorch 1.13.0+cu117 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.11.6 | |