SpaCy - Revision history

BPeat at 03:58, 2 March 2024

2024-03-02T03:58:16Z

BPeat at 11:52, 13 August 2023

2023-08-13T11:52:47Z

BPeat at 00:02, 27 June 2023

2023-06-27T00:02:18Z

BPeat at 19:26, 28 April 2023

2023-04-28T19:26:15Z

BPeat at 18:57, 27 December 2019

2019-12-27T18:57:52Z

BPeat at 00:46, 15 August 2019

2019-08-15T00:46:57Z

BPeat at 22:49, 22 May 2019

2019-05-22T22:49:20Z

BPeat at 02:42, 3 February 2019

2019-02-03T02:42:23Z

BPeat at 12:00, 5 January 2019

2019-01-05T12:00:02Z

BPeat at 11:59, 5 January 2019

2019-01-05T11:59:49Z

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 * [[Python]]
-spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. spaCy v2.0 features new neural models for tagging, parsing and entity recognition. The models have been designed and implemented from scratch specifically for spaCy, to give you an unmatched balance of speed, size and accuracy. A novel bloom [[embedding]] strategy with subword features is used to support huge vocabularies in tiny tables. Convolutional layers with residual connections, layer normalization and maxout non-linearity are used, giving much better efficiency than the standard BiLSTM solution. Finally, the parser and NER use an [[Imitation Learning (IL)]] objective to deliver accuracy in-line with the latest research systems, even when evaluated from raw text. With these innovations, spaCy v2.0's models are 10× smaller, 20% more accurate, and even cheaper to run than the previous generation.
+spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully [[memory]]-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. spaCy v2.0 features new neural models for tagging, parsing and entity recognition. The models have been designed and implemented from scratch specifically for spaCy, to give you an unmatched balance of speed, size and accuracy. A novel bloom [[embedding]] strategy with subword features is used to support huge vocabularies in tiny tables. Convolutional layers with residual connections, layer normalization and maxout non-linearity are used, giving much better efficiency than the standard BiLSTM solution. Finally, the parser and NER use an [[Imitation Learning (IL)]] objective to deliver accuracy in-line with the latest research systems, even when evaluated from raw text. With these innovations, spaCy v2.0's models are 10× smaller, 20% more accurate, and even cheaper to run than the previous generation.
 <youtube>mTyAhRX_FYM</youtube>

@@ Line 15: / Line 15: @@
 * [[Python]]
-spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. spaCy v2.0 features new neural models for tagging, parsing and entity recognition. The models have been designed and implemented from scratch specifically for spaCy, to give you an unmatched balance of speed, size and accuracy. A novel bloom [[embedding]] strategy with subword features is used to support huge vocabularies in tiny tables. Convolutional layers with residual connections, layer normalization and maxout non-linearity are used, giving much better efficiency than the standard BiLSTM solution. Finally, the parser and NER use an imitation learning objective to deliver accuracy in-line with the latest research systems, even when evaluated from raw text. With these innovations, spaCy v2.0's models are 10× smaller, 20% more accurate, and even cheaper to run than the previous generation.
+spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. spaCy v2.0 features new neural models for tagging, parsing and entity recognition. The models have been designed and implemented from scratch specifically for spaCy, to give you an unmatched balance of speed, size and accuracy. A novel bloom [[embedding]] strategy with subword features is used to support huge vocabularies in tiny tables. Convolutional layers with residual connections, layer normalization and maxout non-linearity are used, giving much better efficiency than the standard BiLSTM solution. Finally, the parser and NER use an [[Imitation Learning (IL)]] objective to deliver accuracy in-line with the latest research systems, even when evaluated from raw text. With these innovations, spaCy v2.0's models are 10× smaller, 20% more accurate, and even cheaper to run than the previous generation.
 <youtube>mTyAhRX_FYM</youtube>

@@ Line 15: / Line 15: @@
 * [[Python]]
-spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. spaCy v2.0 features new neural models for tagging, parsing and entity recognition. The models have been designed and implemented from scratch specifically for spaCy, to give you an unmatched balance of speed, size and accuracy. A novel bloom embedding strategy with subword features is used to support huge vocabularies in tiny tables. Convolutional layers with residual connections, layer normalization and maxout non-linearity are used, giving much better efficiency than the standard BiLSTM solution. Finally, the parser and NER use an imitation learning objective to deliver accuracy in-line with the latest research systems, even when evaluated from raw text. With these innovations, spaCy v2.0's models are 10× smaller, 20% more accurate, and even cheaper to run than the previous generation.
+spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. spaCy v2.0 features new neural models for tagging, parsing and entity recognition. The models have been designed and implemented from scratch specifically for spaCy, to give you an unmatched balance of speed, size and accuracy. A novel bloom [[embedding]] strategy with subword features is used to support huge vocabularies in tiny tables. Convolutional layers with residual connections, layer normalization and maxout non-linearity are used, giving much better efficiency than the standard BiLSTM solution. Finally, the parser and NER use an imitation learning objective to deliver accuracy in-line with the latest research systems, even when evaluated from raw text. With these innovations, spaCy v2.0's models are 10× smaller, 20% more accurate, and even cheaper to run than the previous generation.
 <youtube>mTyAhRX_FYM</youtube>

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=SpaCy+deep+machine+learning+ML+artificial+intelligence ...Google search]
-* [[Natural Language Processing (NLP)]]
+* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 * [http://github.com/chartbeat-labs/textacy textacy] ([[Python]]) focuses primarily on the tasks that come before and follow after SpaCy
 * [[Cython]]

@@ Line 10: / Line 10: @@
 * [[Natural Language Processing (NLP)]]
 ** [[Natural Language Tools & Services]]
 * [[Cython]]
 * [http://spacy.io/ spaCy - Industrial-Strength Natural Language Processing]

← Older revision		Revision as of 02:42, 3 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=SpaCy Youtube search...]		[http://www.youtube.com/results?search_query=SpaCy Youtube search...]
		+	[http://www.google.com/search?q=SpaCy+deep+machine+learning+ML+artificial+intelligence ...Google search]
		+

	* [[Natural Language Processing (NLP)]]		* [[Natural Language Processing (NLP)]]