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Hi, I am interested in the prompt proposed in your paper to get sentence embeddings.
I wonder if I can directly use your prompt in other open-source LLM, e.g. LLAMA 3, to get meaningful sentence embe…
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Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word e…
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When exploring relationships *between* topics (2D visualisations, hierarchy) we need to represent *each topic* as a summary vector (cluster-level embedding).
The BERTopic source code stats
> `t…
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2022-06-24 11:31:30.005 | INFO | processors.processor:get_test_data:75 - len of test data:1602
Traceback (most recent call last):
File "test.py", line 476, in
main(args)
File "test.py…
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I experimented a bit with word embeddings and sentence embeddings recently, and put this this (still a bit rough) repo: https://github.com/jorisvandenbossche/wordembeddings. I am opening an issue here…
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## 0. Paper
@inproceedings{naik-etal-2019-exploring,
title = "Exploring Numeracy in Word Embeddings",
author = "Naik, Aakanksha and
Ravichander, Abhilasha and
Rose, Carolyn …
a1da4 updated
4 years ago
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Uses less memory and maybe also faster
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Currently, we're using doc2vec. We should evaluate and compare at least word2vec and doc2vec, and optionally also:
- https://bitbucket.org/omerlevy/hyperwords
- https://nlp.stanford.edu/projects/glo…
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(see syllabus for instructions).
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The goal here is to create a Word2vec (CBOW and SkipGram) Colab tutorial to learn word representations for African languages. We would start with English and then migrate to other languages like Yorub…