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2. Attention Is All You Need - Wikipedia

   en.wikipedia.org/wiki/Attention_Is_All_You_Need

   The name "Transformer" was picked because Jakob Uszkoreit, one of the paper's authors, liked the sound of that word. [9] An early design document was titled "Transformers: Iterative Self-Attention and Processing for Various Tasks", and included an illustration of six characters from the Transformers animated show. The team was named Team ...

3. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   The Transformer architecture, being modular, allows variations. Several common variations are described here. [61] An "encoder-only" Transformer applies the encoder to map an input text into a sequence of vectors that represent the input text. This is usually used for text embedding and representation learning for downstream applications.

4. Transformer - Wikipedia

   en.wikipedia.org/wiki/Transformer

   In electrical engineering, a transformer is a passive component that transfers electrical energy from one electrical circuit to another circuit, or multiple circuits.A varying current in any coil of the transformer produces a varying magnetic flux in the transformer's core, which induces a varying electromotive force (EMF) across any other coils wound around the same core.

5. DBRX - Wikipedia

   en.wikipedia.org/wiki/DBRX

   DBRX is an open-sourced large language model (LLM) developed by Mosaic ML team at Databricks, released on March 27, 2024. [1] [2] [3] It is a mixture-of-experts transformer model, with 132 billion parameters in total. 36 billion parameters (4 out of 16 experts) are active for each token. [4]

6. BERT (language model) - Wikipedia

   en.wikipedia.org/wiki/BERT_(language_model)

   High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules:

7. Word2vec - Wikipedia

   en.wikipedia.org/wiki/Word2vec

   The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).