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
File size: 700 Bytes
2f65125 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"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
}
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