Semanlink - Internet AND Software

Nils Reimers sur X : "Embeddings can store only 1 aspect/topic per embedding well."

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2024-03-13 About

Gabor Melli's Knowledge Base (GM-RKB)

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2023-09-08 About

[2002.06275] TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval

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2023-08-27 About

Google "We Have No Moat, And Neither Does OpenAI"

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> low-cost public involvement was enabled
by a vastly cheaper mechanism for fine tuning called low
rank adaptation ()[LoRA](tag:lora)

> **Part of what makes LoRA so effective is that ... it’s stackable.**
>
> By contrast, training giant models from scratch not only
throws away the pretraining, but also any iterative
improvements that have been made on top.

> LoRA updates are very cheap to produce (~$100) for the
most popular model sizes.

> Many of these projects are saving time by training on
small, highly curated datasets...
> These
datasets are built using synthetic methods (e.g. filtering
the best responses from an existing model) and
scavenging from other projects

> Directly Competing With Open Source
Is a Losing Proposition

> Paradoxically, the one clear winner in all of this is Meta.
Because the leaked model was theirs ([LLaMA](tag:llama)), they have
effectively garnered an entire planet's worth of free labor.
Since most open source innovation is happening on top of
their architecture, there is nothing stopping them from
directly incorporating it into their products.

2023-05-04 About

The Embedding Archives: Millions of Wikipedia Article Embeddings in Many Languages

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2023-04-21 About

Tutorial: VS Code with Google Cloud AI Platform as a backend | by Kyle Ziegler | Medium

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2023-03-21 About

Jim Fan sur Twitter : "Do you know that DeepMind has actually open-sourced the heart of AlphaGo & AlphaZero?... "

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2023-02-15 About

Ramsri Goutham Golla sur Twitter : "The most practical open-source competitor to @OpenAI 's GPT-3 is Google's Flan-T5 Here are 5 Flan-T5 resources to try out easily, deploy, or fine-tune it! 🧵" / Twitter

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2023-02-04 About

Apache Jena - RDF Patch

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Jena

2023-01-03 About

Sparql Secrets In Jena-Fuseki - DataScienceCentral.com

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Jena Fuseki

2022-07-26 About

Unveiling Transformers with LEGO - YouTube

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2022-06-30 About

[2004.05119] Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer

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2022-03-31 About

Wiki History Game

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2022-01-16 About

facebookresearch/DrQA: Reading Wikipedia to Answer Open-Domain Questions

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2021-12-08 About

Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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2021-12-05 About

Calling your own JavaScript functions from SPARQL queries

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2021-06-07 About

Projet:Requêter le Wiktionnaire — Wiktionnaire

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2021-04-12 About

[2007.15779] Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

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2021-04-11 About

ringgaard/sling: SLING - A natural language frame semantics parser

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2021-03-08 About

Wikidata browser

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Wikidata browser

2021-03-06 About

TAGME: on-the-fly annotation of short text fragments!

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2020-10-11 About

[1909.01259] Neural Attentive Bag-of-Entities Model for Text Classification

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A model that performs **text classification using entities in a knowledge base**.

> Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine **simple high-recall entity detection based on a dictionary** (word->list of entities), to detect entities in a document, with a novel neural **attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities**.

2 steps:

1. Entity detection
2. Classification using the detected entities (+text) as inputs

Regarding entity linking, a local model which uses cosine
similarity between the embedding of the target
entity and the word-based representation of
the document to capture the relevance of an entity
given a document.

Embeddings from the KB: computed using [#Wikipedia2Vec](tag:wikipedia2vec) (similar words and entities
close to one another in a unified vector space)

Model using attention, with 2 features :

- cosine similarity between the
embedding of the entity and the word based
representation of the document
- the probability that the entity
name refers to the entity in KB.

Somewhat [related](doc:2020/01/investigating_entity_knowledge_)

### Conclusion:

>a neural
network model that performs text classification using
entities in Wikipedia. We combined simple
dictionary-based entity detection with a neural attention
mechanism to enable the model to focus
on a small number of unambiguous and relevant
entities in a document.

2020-09-02 About

[1812.06280] Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia

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2020-09-02 About

Sandstorm

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2020-07-18 About

Abstract Wikipedia/July 2020 announcement

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2020-07-03 About

Lord of the Wiki Ring: Importing Wikidata into Neo4j and analyzing family trees

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