Comments (7)
Hmmm, it might improve if you increase the keyphrase_ngram_range
to (1, 3) for example. However, this is exactly what can happen with KeyBERT. It is highly dependent on the underlying embedding model. For that reason, I typically like to use embedding models that are trained mostly for similarity measures, like those used in sentence-transformers (e.g., xlm-r-bert-base-nli-stsb-mean-tokens).
All in all, it requires some experimentation with models until you find the one best suited to your use-case.
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Yes! Simply use KeyBERT(model='xlm-r-bert-base-nli-stsb-mean-tokens')
to use the multi-lingual model. There are several models that you could use here. However, the model that you referenced is the one I would suggest for any language other than English.
Also, you are in luck! A few days ago I released the v0.2 of KeyBERT which includes Flair. Flair allows you to, essentially, use any 🤗 transformers model. Thus, if you find any good French model, then you could also use that!
from keybert import KeyBERT
from flair.embeddings import TransformerDocumentEmbeddings
roberta = TransformerDocumentEmbeddings('roberta-base')
model = KeyBERT(model=roberta)
Then, instead of roberta-base
, you can use any model from this list.
from keybert.
Thank you for your help. I am a beginner with transformer model.
I found these 2 french models
- camembert-base : CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
- abhilash1910/french-roberta : This is a Masked Language Model trained with Roberta on a small French News Corpus(Leipzig corpora).
Do you think I can test with these 2 models?
from keybert.
Although I have not tested these models myself, I do believe CamemBERT is used quite often for French texts. It should be no problem if you were to use those models. I am quite curious myself what the performance is, so let me know if you find out!
from keybert.
If you are interested, I can share in this issue, my code comparing the results of keyword extraction with
- TF/IDF
- Yake
- KeyBert
from keybert.
That would be great! I cannot read French well, so I would be dependent on your opinion regarding the quality of the keywords 😄
from keybert.
Compare keyword extraction results, in French language, from TF/IDF, Yake, KeyBert: https://gist.github.com/LeMoussel/aa5f7c46c6cb09473e97b20eb2e13cc4
The quality of keywords with KeyBert seems "strange" to me. 🤔
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Related Issues (20)
- KeyLLM - Mistral token issue HOT 2
- Running Keybert for a list of docs to extract arabic keywords HOT 1
- KeyLLM - page_content error with bedrock model HOT 3
- Efficient KeyLLM + KeyBERT - Torch not compiled with CUDA enabled HOT 1
- 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
- keybert benchmarks with respect to other phrase extraction techniques. HOT 1
- Drop support for Python < 3.8 HOT 5
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