Feature Extraction
Transformers
Safetensors
PyTorch
English
eden
text-enhancement
grammar-correction
text-rewriting
encoder-decoder
transformer
custom_code
Instructions to use Rybib/EDEN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rybib/EDEN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Rybib/EDEN", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Rybib/EDEN", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "EdenForTextEnhancement" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_eden.EdenConfig", | |
| "AutoModel": "modeling_eden.EdenForTextEnhancement" | |
| }, | |
| "beam_size": 4, | |
| "bos_token_id": 2, | |
| "d_model": 640, | |
| "dim_feedforward": 2560, | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "eos_token_id": 3, | |
| "hidden_size": 640, | |
| "is_encoder_decoder": true, | |
| "length_penalty": 0.7, | |
| "max_len": 512, | |
| "model_type": "eden", | |
| "n_heads": 10, | |
| "n_layers": 8, | |
| "num_attention_heads": 10, | |
| "num_hidden_layers": 8, | |
| "pad_token_id": 1, | |
| "repetition_penalty": 1.08, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.9.0", | |
| "unk_token_id": 0, | |
| "vocab_size": 24000 | |
| } | |