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adding spacy-universal-sentence-encoder (#5534)
* adding spacy-universal-sentence-encoder * update affiliation * updated code example
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.github/contributors/MartinoMensio.md
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.github/contributors/MartinoMensio.md
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@ -99,8 +99,8 @@ mark both statements:
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| Field | Entry |
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| Field | Entry |
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|------------------------------- | -------------------- |
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|------------------------------- | -------------------- |
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| Name | Martino Mensio |
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| Name | Martino Mensio |
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| Company name (if applicable) | Polytechnic University of Turin |
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| Company name (if applicable) | The Open University |
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| Title or role (if applicable) | Student |
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| Title or role (if applicable) | PhD Student |
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| Date | 17 November 2017 |
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| Date | 17 November 2017 |
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| GitHub username | MartinoMensio |
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| GitHub username | MartinoMensio |
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| Website (optional) | https://martinomensio.github.io/ |
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| Website (optional) | https://martinomensio.github.io/ |
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@ -1,5 +1,29 @@
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{
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{
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"resources": [
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"resources": [
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{
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"id": "spacy-universal-sentence-encoder",
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"title": "SpaCy - Universal Sentence Encoder",
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"slogan": "Make use of Google's Universal Sentence Encoder directly within SpaCy",
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"description": "This library lets you use Universal Sentence Encoder embeddings of Docs, Spans and Tokens directly from TensorFlow Hub",
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"github": "MartinoMensio/spacy-universal-sentence-encoder-tfhub",
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"code_example": [
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"import spacy_universal_sentence_encoder",
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"load one of the models: ['en_use_md', 'en_use_lg', 'xx_use_md', 'xx_use_lg']",
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"nlp = spacy_universal_sentence_encoder.load_model('en_use_lg')",
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"# get two documents",
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"doc_1 = nlp('Hi there, how are you?')",
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"doc_2 = nlp('Hello there, how are you doing today?')",
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"# use the similarity method that is based on the vectors, on Doc, Span or Token",
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"print(doc_1.similarity(doc_2[0:7]))"
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],
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"category": ["models", "pipeline"],
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"author": "Martino Mensio",
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"author_links": {
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"twitter": "MartinoMensio",
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"github": "MartinoMensio",
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"website": "https://martinomensio.github.io"
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}
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},
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{
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{
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"id": "whatlies",
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"id": "whatlies",
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"title": "whatlies",
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"title": "whatlies",
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