Instructions to use Falah/Mask_awesome_eli5_mlm_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falah/Mask_awesome_eli5_mlm_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Falah/Mask_awesome_eli5_mlm_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Falah/Mask_awesome_eli5_mlm_model") model = AutoModelForMaskedLM.from_pretrained("Falah/Mask_awesome_eli5_mlm_model") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: Mask_awesome_eli5_mlm_model | |
| 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. --> | |
| # Mask_awesome_eli5_mlm_model | |
| This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.9796 | |
| ## 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: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.7361 | 1.0 | 1131 | 2.0661 | | |
| | 1.8475 | 2.0 | 2262 | 2.0314 | | |
| | 1.983 | 3.0 | 3393 | 2.0085 | | |
| | 2.0677 | 4.0 | 4524 | 1.9931 | | |
| ### Framework versions | |
| - Transformers 4.27.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.9.0 | |
| - Tokenizers 0.13.3 | |