Added spacy-wrap to universe
* Added spacy-wrap to universe
Added spacy-wrap to universe a small package for wrapping fine-tuned huggingface transformers to a spacy pipeline following the same API as spacy-transformers. (Currently limited to classification models)
* Update website/meta/universe.json
* Update website/meta/universe.json
* Update website/meta/universe.json
* Update website/meta/universe.json
"category": ["pipeline"],
"tags": ["pipeline", "danish"]
},
+ {
+ "id": "spacy-wrap",
+ "title": "spaCy-wrap",
+ "slogan": "For Wrapping fine-tuned transformers in spaCy pipelines",
+ "description": "spaCy-wrap is a wrapper library for spaCy for including fine-tuned transformers from Huggingface in your spaCy pipeline allowing inclusion of existing models within existing workflows.",
+ "github": "kennethenevoldsen/spacy-wrap",
+ "pip": "spacy_wrap",
+ "code_example": [
+ "import spacy",
+ "import spacy_wrap",
+ "",
+ "nlp = spacy.blank('en')",
+ "config = {",
+ " 'doc_extension_trf_data': 'clf_trf_data', # document extention for the forward pass",
+ " 'doc_extension_prediction': 'sentiment', # document extention for the prediction",
+ " 'labels': ['negative', 'neutral', 'positive'],",
+ " 'model': {",
+ " 'name': 'cardiffnlp/twitter-roberta-base-sentiment', # the model name or path of huggingface model",
+ "},",
+ "}",
+ "",
+ "transformer = nlp.add_pipe('classification_transformer', config=config)",
+ "transformer.model.initialize()",
+ "",
+ "doc = nlp('spaCy is a wonderful tool')",
+ "",
+ "print(doc._.clf_trf_data)",
+ "# TransformerData(wordpieces=...",
+ "print(doc._.sentiment)",
+ "# 'positive'",
+ "print(doc._.sentiment_prob)",
+ "# {'prob': array([0.004, 0.028, 0.969], dtype=float32), 'labels': ['negative', 'neutral', 'positive']}"
+ ],
+ "thumb": "https://raw.githubusercontent.com/KennethEnevoldsen/spacy-wrap/main/docs/_static/icon.png",
+ "author": "Kenneth Enevoldsen",
+ "author_links": {
+ "github": "KennethEnevoldsen",
+ "website": "https://www.kennethenevoldsen.com"
+ },
+ "category": ["pipeline", "models", "training"],
+ "tags": ["pipeline", "models", "transformers"]
+ },
{
"id": "textdescriptives",
"title": "TextDescriptives",
