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Added spacy-wrap to universe (#10168)
* 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 Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
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@ -978,6 +978,48 @@
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"category": ["pipeline"],
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"tags": ["pipeline", "danish"]
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},
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{
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"id": "spacy-wrap",
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"title": "spaCy-wrap",
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"slogan": "For Wrapping fine-tuned transformers in spaCy pipelines",
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"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.",
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"github": "kennethenevoldsen/spacy-wrap",
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"pip": "spacy_wrap",
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"code_example": [
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"import spacy",
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"import spacy_wrap",
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"",
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"nlp = spacy.blank('en')",
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"config = {",
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" 'doc_extension_trf_data': 'clf_trf_data', # document extention for the forward pass",
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" 'doc_extension_prediction': 'sentiment', # document extention for the prediction",
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" 'labels': ['negative', 'neutral', 'positive'],",
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" 'model': {",
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" 'name': 'cardiffnlp/twitter-roberta-base-sentiment', # the model name or path of huggingface model",
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"},",
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"}",
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"",
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"transformer = nlp.add_pipe('classification_transformer', config=config)",
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"transformer.model.initialize()",
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"",
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"doc = nlp('spaCy is a wonderful tool')",
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"",
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"print(doc._.clf_trf_data)",
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"# TransformerData(wordpieces=...",
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"print(doc._.sentiment)",
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"# 'positive'",
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"print(doc._.sentiment_prob)",
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"# {'prob': array([0.004, 0.028, 0.969], dtype=float32), 'labels': ['negative', 'neutral', 'positive']}"
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],
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"thumb": "https://raw.githubusercontent.com/KennethEnevoldsen/spacy-wrap/main/docs/_static/icon.png",
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"author": "Kenneth Enevoldsen",
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"author_links": {
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"github": "KennethEnevoldsen",
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"website": "https://www.kennethenevoldsen.com"
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},
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"category": ["pipeline", "models", "training"],
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"tags": ["pipeline", "models", "transformers"]
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},
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{
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"id": "textdescriptives",
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"title": "TextDescriptives",
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