Instructions to use Billwzl/20split_dataset_version3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Billwzl/20split_dataset_version3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Billwzl/20split_dataset_version3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Billwzl/20split_dataset_version3") model = AutoModelForMaskedLM.from_pretrained("Billwzl/20split_dataset_version3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: 20split_dataset_version3 | |
| 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. --> | |
| # 20split_dataset_version3 | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.8310 | |
| ## 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: 5e-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: 16 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 3.1679 | 1.0 | 313 | 2.9768 | | |
| | 2.9869 | 2.0 | 626 | 2.9299 | | |
| | 2.8528 | 3.0 | 939 | 2.9176 | | |
| | 2.7435 | 4.0 | 1252 | 2.9104 | | |
| | 2.6458 | 5.0 | 1565 | 2.8863 | | |
| | 2.5865 | 6.0 | 1878 | 2.8669 | | |
| | 2.5218 | 7.0 | 2191 | 2.8802 | | |
| | 2.4647 | 8.0 | 2504 | 2.8639 | | |
| | 2.3933 | 9.0 | 2817 | 2.8543 | | |
| | 2.3687 | 10.0 | 3130 | 2.8573 | | |
| | 2.3221 | 11.0 | 3443 | 2.8398 | | |
| | 2.276 | 12.0 | 3756 | 2.8415 | | |
| | 2.2379 | 13.0 | 4069 | 2.8471 | | |
| | 2.2427 | 14.0 | 4382 | 2.8318 | | |
| | 2.1741 | 15.0 | 4695 | 2.8356 | | |
| | 2.1652 | 16.0 | 5008 | 2.8310 | | |
| ### Framework versions | |
| - Transformers 4.21.0 | |
| - Pytorch 1.12.0+cu113 | |
| - Datasets 2.4.0 | |
| - Tokenizers 0.12.1 | |