Comments (1)
I believe there are a couple of papers that touch on this but I'm not sure which ones. Typically, they are the ones that attempt to benchmark against KeyBERT to showcase that they perform better. Note that this is typically from a "vanilla" KeyBERT perspective and often with tricks like MMR or the newer KeyLLM. You can find all papers that cite KeyBERT here.
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Related Issues (20)
- Allow KeyBERT to pass `batch_size` to `llm.encode()` method HOT 5
- Make system content as variable HOT 2
- Fail to parse OpenAI api response HOT 2
- Extract keywords from multiple documents given a nested list of candidates for each document. HOT 1
- can't import keybert HOT 9
- Using KeyBERT with a locally saved model HOT 1
- Not able to use gensim HOT 5
- Why and how the same model for doc_embeddings and word_embeddings? HOT 1
- Setup check. Script to get keywords for comparing against SimpleMaths, TextRank and Philology results HOT 5
- Add more LLMs HOT 1
- OSError: libcudart.so.12: cannot open shared object file: No such file or directory HOT 2
- KeyLLM error: AttributeError: 'str' object has no attribute 'completions' HOT 9
- KeyLLM Error: AttributeError: 'CompletionChoice' object has no attribute 'message' HOT 6
- CLI tool like YAKE
- Drop support for Python < 3.8 HOT 5
- pre-commit-hooks: replace black and flake8 HOT 2
- OpenAi api HOT 4
- How to get the keywords' embedding? HOT 5
- max token with openAI HOT 1
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