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Train Word embeddings from scratch with Nessvec and PyTorch.
Subjects: Get full text
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Use PyNNDescent and `nessvec` to index high dimensional vectors (word embeddings).
Subjects: Get full text
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Behind the scenes at the Natural History Museum /
Subjects: “…American Museum of Natural History.…”
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Working with transformer-based embeddings for text similarity matching.
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National conservation /
Subjects: “…Conservation of natural resources.…”
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Voices of the land /
Subjects: “…Nature Religious aspects.…”
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NLP in action : attention mechanism and upgrading TorchText 0.9.
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Become an outdoor photographer.
Subjects: “…Nature photography.…”
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Become an outdoor photographer.
Subjects: “…Nature photography.…”
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Quick guide to ChatGPT, embeddings, and other large language models (LLMs).
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