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We should download pre-trained word embeddings to improve our sentiment analysis. This lets us leverage language information from a massive text database, so our neural network can focus on analyzing …
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This line structures a binary model (from Gensim) to a text file.
I think that we need to adapt all the other functions (main_train.train(), word2term.wordVST2TermVST() and so all word2term.py functi…
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Hi,
Setting Dropout in production results in random embeddings. This has been discussed in the main repo if you search. To reproduce this, just run the same string more than once, each time you'll s…
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We (AUEB's NLP group: http://nlp.cs.aueb.gr/) recently released word embeddings pre-trained on text from 27 million biomedical articles from the MEDLINE/PubMed Baseline 2018.
Two versions of word e…
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Hi, I am trying to use the embeddings.lua script to generate pre-trained word embeddings. However, after running something like:
th tools/embeddings.lua -lang en -dict_file data/demo.src.dict -save…
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The two themes in this project will be:
1) using word embeddings in sentiment analysis. As a starting point, see:
- [this article](https://www.sciencedirect.com/science/article/pii/S18770509183076…
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https://research.googleblog.com/2016/12/open-sourcing-embedding-projector-tool.html
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I am trying to generate emotion specific word embeddings with a similar spproach as SSWE. The sentiment is a three class label, positive negative and neutral.
My emotion set has eight classes. Can you…
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bash scripts/merge_lora.sh
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Loading checkpoint shards: 100%|███| 2/2 [01:02
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- Start with word embeddings. Can we cluster to find marketing language?
- Validate this “top-down” dictionary creation with “bottom-up” dictionary automation (e.g., using support vector machines or …