Instructions to use vppvgit/BiblItBERT-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vppvgit/BiblItBERT-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vppvgit/BiblItBERT-1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vppvgit/BiblItBERT-1") model = AutoModelForMaskedLM.from_pretrained("vppvgit/BiblItBERT-1") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - null | |
| model-index: | |
| - name: BiblItBERT-1 | |
| results: | |
| - task: | |
| name: Masked Language Modeling | |
| type: fill-mask | |
| <!-- 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. --> | |
| # BiblItBERT-1 | |
| This model is a fine-tuned version of [vppvgit/BiblItBERT](https://huggingface.co/vppvgit/BiblItBERT) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7775 | |
| ## 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: 0 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 50 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:------:|:---------------:| | |
| | 1.5764 | 1.0 | 16528 | 1.5214 | | |
| | 1.4572 | 2.0 | 33056 | 1.4201 | | |
| | 1.3787 | 3.0 | 49584 | 1.3728 | | |
| | 1.3451 | 4.0 | 66112 | 1.3245 | | |
| | 1.3066 | 5.0 | 82640 | 1.2614 | | |
| | 1.2447 | 6.0 | 99168 | 1.2333 | | |
| | 1.2172 | 7.0 | 115696 | 1.2149 | | |
| | 1.2079 | 8.0 | 132224 | 1.1853 | | |
| | 1.2167 | 9.0 | 148752 | 1.1586 | | |
| | 1.2056 | 10.0 | 165280 | 1.1503 | | |
| | 1.1307 | 11.0 | 181808 | 1.1224 | | |
| | 1.1689 | 12.0 | 198336 | 1.1074 | | |
| | 1.1007 | 13.0 | 214864 | 1.0924 | | |
| | 1.0901 | 14.0 | 231392 | 1.0659 | | |
| | 1.0667 | 15.0 | 247920 | 1.0650 | | |
| | 1.0434 | 16.0 | 264448 | 1.0362 | | |
| | 1.0333 | 17.0 | 280976 | 1.0250 | | |
| | 1.0342 | 18.0 | 297504 | 1.0198 | | |
| | 1.0059 | 19.0 | 314032 | 0.9950 | | |
| | 0.9719 | 20.0 | 330560 | 0.9836 | | |
| | 0.9863 | 21.0 | 347088 | 0.9873 | | |
| | 0.9781 | 22.0 | 363616 | 0.9724 | | |
| | 0.9369 | 23.0 | 380144 | 0.9599 | | |
| | 0.9578 | 24.0 | 396672 | 0.9557 | | |
| | 0.9253 | 25.0 | 413200 | 0.9400 | | |
| | 0.9441 | 26.0 | 429728 | 0.9222 | | |
| | 0.9138 | 27.0 | 446256 | 0.9140 | | |
| | 0.882 | 28.0 | 462784 | 0.9045 | | |
| | 0.864 | 29.0 | 479312 | 0.8880 | | |
| | 0.8632 | 30.0 | 495840 | 0.9023 | | |
| | 0.8342 | 32.0 | 528896 | 0.8740 | | |
| | 0.8037 | 34.0 | 561952 | 0.8647 | | |
| | 0.8119 | 37.0 | 611536 | 0.8358 | | |
| | 0.8011 | 38.0 | 628064 | 0.8252 | | |
| | 0.786 | 39.0 | 644592 | 0.8228 | | |
| | 0.7697 | 41.0 | 677648 | 0.8138 | | |
| | 0.7485 | 42.0 | 694176 | 0.8104 | | |
| | 0.7689 | 43.0 | 710704 | 0.8018 | | |
| | 0.7401 | 45.0 | 743760 | 0.7957 | | |
| | 0.7031 | 47.0 | 776816 | 0.7726 | | |
| | 0.7578 | 48.0 | 793344 | 0.7864 | | |
| | 0.7298 | 49.0 | 809872 | 0.7775 | | |
| ### Framework versions | |
| - Transformers 4.10.3 | |
| - Pytorch 1.9.0+cu102 | |
| - Datasets 1.12.1 | |
| - Tokenizers 0.10.3 | |