syGOAT

> it might be worthwhile to combine all words to a single sentence and create an embedding out of that but that is for a future version. @MaartenGr Has the...

@MaartenGr I have fully understood. Thank you for your reply!

Thanks for your explanation! Maybe I've understood your point. In the original paper of TIES-Merging: https://arxiv.org/abs/2306.01708, TIES contains 3 steps: Trim, Elect, Disjoint Merge. Trim keeps the top k%. Elect...

> so `._extract_representative_docs` should work. @MaartenGr I couldn't find an example in BERTopic doc. Could you please provide an example?

I have read this FAQ: https://maartengr.github.io/BERTopic/faq.html#how-do-i-calculate-the-probabilities-of-all-topics-in-a-document and known why my probabilities is `None`. Thank you so much for making this useful library! In addtion, `.approximate_distribution` models the distribution of topics...

@MaartenGr Thankx for your reply! Would you mind answering another question of mine? > could you please add a parameter to let users set top k documents to show probabilities...

> If someone is interested in working on this, this can be added as an additional parameter in the `get_representative_docs` parameter as a way to recalculate the most representative document....

Thank you for your reply and for your contribution to the application of clustering!

> Note that with 16 bits, such score collisions are quite common, especially with the larger vocab sizes. For the score collisions with 16bits, could you please give some examples,...

我也遇到了这个问题。Mistral-7b-instruct-v0.2 在 4*4090 训练一段时间后 OOM，sft lora。 ``` 82%|████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 7060/8660 [5:05:53