Difference between revisions of "SpaCy"
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| − | 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 | + | 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> | <youtube>mTyAhRX_FYM</youtube> | ||
Latest revision as of 22:58, 1 March 2024
Youtube search... ...Google search
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- textacy (Python) focuses primarily on the tasks that come before and follow after SpaCy
- Cython
- spaCy - Industrial-Strength Natural Language Processing
- Feature Exploration/Learning
- 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.
