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: 705 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 | [build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"
[project]
name = "eden-text-enhancement"
version = "1.0.0"
description = "EDEN: a from-scratch encoder-decoder Transformer for text enhancement."
readme = "README.md"
requires-python = ">=3.9"
license = { text = "Apache-2.0" }
authors = [{ name = "Ryan Dunn" }]
keywords = ["nlp", "text-enhancement", "grammar-correction", "transformer", "pytorch"]
dependencies = [
"torch>=2.1",
"transformers>=4.40",
"tokenizers>=0.15",
"safetensors>=0.4",
"numpy>=1.24",
"datasets>=2.16",
"tqdm>=4.66",
"psutil>=5.9",
]
[project.scripts]
eden = "eden.cli:main"
[tool.setuptools]
packages = ["eden"]
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