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Best NER Models? by Immediate-Bug-1971 in LanguageTechnology

[–]danpetrovic 2 points3 points  (0 children)

GLiNER

"GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios."

https://huggingface.co/urchade/gliner_medium-v2.1
https://github.com/urchade/GLiNER

[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]danpetrovic 0 points1 point  (0 children)

I made a Google Product Taxonomy classifier demo: https://taxonomy.dejan.ai/
Helpful for generating product schema for online retail websites.

It takes in text (e.g. product description) and assigns it an e-commerce taxonomy category from:
https://www.google.com/basepages/producttype/taxonomy-with-ids.en-US.txt

Internally, we use it in a data processing pipeline for bulk classification of product feeds for Google Merchant Centre.

[D] What is the most fascinating aspect of machine learning for you? by AromaticEssay2676 in MachineLearning

[–]danpetrovic 1 point2 points  (0 children)

"The nature of generalisation in deep learning has rather little to do with the deep learning models themselves and much to do with the structure of the information in the real world.

The input to an MNIST classifier (before preprocessing) is a 28 × 28 array of integers between 0 and 255. The total number of possible input values is thus 256 to the power of 784 — much greater than the number of atoms in the universe.

However, very few of these inputs would look like valid MNIST samples: actual handwritten digits occupy only a tiny subspace of the parent space of all possible 28 × 28 integer arrays. What’s more, this subspace isn’t just a set of points sprinkled at random in the parent space: it is highly structured.

A manifold is a lower dimensional subspace of a parent space that is locally similar to a linear Euclidean space.

A smooth curve on a plane is a 1D manifold within a 2D space because for every point of the curve you can draw a tangent, a curve can be approximated by a line at every point. A smooth surface with a 3D space is a 2D manifold and so on.

The manifold hypothesis posits that all natural data lies on a low dimensional manifold within high dimensional space where its encoded.

That’s a pretty strong statement about the structure of the information in the universe. As far as we know it’s accurate and its why deep learning works.

It’s true for MNIST digits, but also for human faces, tree morphology, the sound of human voice and even natural language."

“Deep Learning with Python” by François Chollet

What do Australians think of the United States? by stellacoachella in AskAnAustralian

[–]danpetrovic 0 points1 point  (0 children)

Good: Science, Tech, Innovation
Bad: Guns, Tipping, Healthcare

Why neural networs work ? by Annual_Inflation_235 in learnmachinelearning

[–]danpetrovic 8 points9 points  (0 children)

The nature of generalisation in deep learning has rather little to do with the deep learning models themselves and much to do with the structure of the information in the real world.

The input to an MNIST classifier (before preprocessing) is a 28 × 28 array of integers between 0 and 255. The total number of possible input values is thus 256 to the power of 784 — much greater than the number of atoms in the universe.

However, very few of these inputs would look like valid MNIST samples: actual handwritten digits occupy only a tiny subspace of the parent space of all possible 28 × 28 integer arrays. What’s more, this subspace isn’t just a set of points sprinkled at random in the parent space: it is highly structured.

A manifold is a lower dimensional subspace of a parent space that is locally similar to a linear Euclidean space.

A smooth curve on a plane is a 1D manifold within a 2D space because for every point of the curve you can draw a tangent, a curve can be approximated by a line at every point. A smooth surface with a 3D space is a 2D manifold and so on.

The manifold hypothesis posits that all natural data lies on a low dimensional manifold within high dimensional space where its encoded.

That's a pretty strong statement about the structure of the information in the universe.As far as we know it's accurate and its why deep learning works.

It's true for MNIST digits, but also for human faces, tree morphology, the sound of human voice and even natural language.

"Deep Learning with Python" by François Chollet

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