HITgram: A Platform for Experimenting with n-gram Language Models
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| Publié dans: | arXiv.org (Dec 14, 2024), p. n/a |
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| Auteur principal: | |
| Autres auteurs: | , , , , |
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Cornell University Library, arXiv.org
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| Sujets: | |
| Accès en ligne: | Citation/Abstract Full text outside of ProQuest |
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| Résumé: | Large language models (LLMs) are powerful but resource intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimentation, ideal for resource-constrained environments. It supports unigrams to 4-grams and incorporates features like context sensitive weighting, Laplace smoothing, and dynamic corpus management to e-hance prediction accuracy, even for unseen word sequences. Experiments demonstrate HITgram's efficiency, achieving 50,000 tokens/second and generating 2-grams from a 320MB corpus in 62 seconds. HITgram scales efficiently, constructing 4-grams from a 1GB file in under 298 seconds on an 8 GB RAM system. Planned enhancements include multilingual support, advanced smoothing, parallel processing, and model saving, further broadening its utility. |
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| ISSN: | 2331-8422 |
| Source: | Engineering Database |