Instructions to use Billwzl/20split_dataset_version2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Billwzl/20split_dataset_version2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Billwzl/20split_dataset_version2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Billwzl/20split_dataset_version2") model = AutoModelForMaskedLM.from_pretrained("Billwzl/20split_dataset_version2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: 20split_dataset_version2 | |
| 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_version2 | |
| 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: 2.0626 | |
| ## 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: 64 | |
| - eval_batch_size: 64 | |
| - 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 | | |
| |:-------------:|:-----:|:------:|:---------------:| | |
| | 2.7621 | 1.0 | 11851 | 2.5216 | | |
| | 2.5466 | 2.0 | 23702 | 2.4157 | | |
| | 2.4505 | 3.0 | 35553 | 2.3592 | | |
| | 2.3798 | 4.0 | 47404 | 2.3028 | | |
| | 2.3178 | 5.0 | 59255 | 2.2768 | | |
| | 2.272 | 6.0 | 71106 | 2.2366 | | |
| | 2.2323 | 7.0 | 82957 | 2.2128 | | |
| | 2.1928 | 8.0 | 94808 | 2.1797 | | |
| | 2.157 | 9.0 | 106659 | 2.1667 | | |
| | 2.1292 | 10.0 | 118510 | 2.1392 | | |
| | 2.0978 | 11.0 | 130361 | 2.1280 | | |
| | 2.0725 | 12.0 | 142212 | 2.1106 | | |
| | 2.052 | 13.0 | 154063 | 2.0944 | | |
| | 2.0268 | 14.0 | 165914 | 2.0804 | | |
| | 2.0121 | 15.0 | 177765 | 2.0698 | | |
| | 1.9997 | 16.0 | 189616 | 2.0626 | | |
| ### Framework versions | |
| - Transformers 4.20.1 | |
| - Pytorch 1.12.0+cu113 | |
| - Datasets 2.4.0 | |
| - Tokenizers 0.12.1 | |