Instructions to use Shamima/diffusion_prompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shamima/diffusion_prompt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Shamima/diffusion_prompt")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Shamima/diffusion_prompt") model = AutoModelForMaskedLM.from_pretrained("Shamima/diffusion_prompt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| base_model: distilroberta-base | |
| datasets: | |
| - diffusiondb | |
| model-index: | |
| - name: diffusion_prompt | |
| 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. --> | |
| # diffusion_prompt | |
| This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the diffusiondb dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.3442 | |
| ## 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: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 171 | 1.5182 | | |
| | No log | 2.0 | 342 | 1.4020 | | |
| | 1.6418 | 3.0 | 513 | 1.3474 | | |
| ### Framework versions | |
| - Transformers 4.41.2 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.19.2 | |
| - Tokenizers 0.19.1 | |