princeton-nlp/TRIME
[EMNLP 2022] Training Language Models with Memory Augmentation https://arxiv.org/abs/2205.12674
This project helps machine learning engineers and researchers improve how language models understand and generate text by enabling them to use more context. It takes in existing language models and training data, and outputs models that are better at predicting the next word, even with long and complex inputs. This is useful for anyone working on tasks like text generation, summarization, or translation who needs more accurate and context-aware language models.
195 stars. No commits in the last 6 months.
Use this if you are a machine learning practitioner looking to enhance the performance and contextual understanding of your language models for tasks like natural language generation or machine translation.
Not ideal if you are looking for a pre-packaged, end-user application for language model deployment, as this is a research-focused toolkit requiring technical expertise.
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Python
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Last pushed
Jun 14, 2023
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