electra
MUSE
electra | MUSE | |
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3 | 4 | |
2,296 | 3,128 | |
0.7% | - | |
0.0 | 0.0 | |
about 1 month ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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electra
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Fine-tuned model consistently producing Precision and Recall scores of 0 from start of training, any suggestions on how to improve?
If this is your own implementation of ELECTRA, hopefully you have previous versions you've demonstrated working, you could revert back to a working version, then apply the changes you made one-by-one. If it's open-source code you are using, such as this one, try and find a working example, run it yourself, carefully modify it, preserve it in a working (high performance) state, change it piece-by-piece until it works on your problem.
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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators Web Demo
github: https://github.com/google-research/electra
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Help with aligned word embeddings
If you have at least a decent gaming gpu or want to bother with colab, you could get a relevant dataset and use electra https://github.com/google-research/electra
MUSE
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The Illustrated Word2Vec
This is a great guide.
Also - despite the fact that language model embedding [1] are currently the hot rage, good old embedding models are more than good enough for most tasks.
With just a bit of tuning, they're generally as good at many sentence embedding tasks [2], and with good libraries [3] you're getting something like 400k sentence/sec on laptop CPU versus ~4k-15k sentences/sec on a v100 for LM embeddings.
When you should use language model embeddings:
- Multilingual tasks. While some embedding models are multilingual aligned (eg. MUSE [4]), you still need to route the sentence to the correct embedding model file (you need something like langdetect). It's also cumbersome, with one 400mb file per language.
For LM embedding models, many are multilingual aligned right away.
- Tasks that are very context specific or require fine-tuning. For instance, if you're making a RAG system for medical documents, the embedding space is best when it creates larger deviations for the difference between seemingly-related medical words.
This means models with more embedding dimensions, and heavily favors LM models over classic embedding models.
1. sbert.net
2. https://collaborate.princeton.edu/en/publications/a-simple-b...
3. https://github.com/oborchers/Fast_Sentence_Embeddings
4. https://github.com/facebookresearch/MUSE
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Best AI-generated bilingual dictionaries
I am looking for the best way to get an AI-generated bilingual dictionary, so that I can get a list of words with their translations for each language pair I want. It is possible to get a list (with sometimes alright, sometimes bad results) using this project. Additionally, there exists this, but it does not have a whole lot of words unfortunately. I also read about the huge CCMatrix dataset which has millions of parallel sentences for many language pairs, but how would I extract direct word translations from it? (A naive python algorithm would probably take forever.)
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Help with aligned word embeddings
We currently train our own vocabularies on Wikipedia and other sources, and we align the vocabularies using MUSE with default settings (0-5000 dictionary for training, 5000-6500 dictionary for evaluation and 5 refinements).
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D How Advanced Is The Current Practice Of
MUSE embeddings has an unsupervised approach based on adversarial training: https://github.com/facebookresearch/MUSE#the-unsupervised-way-adversarial-training-and-refinement-cpugpu
What are some alternatives?
clip-as-service - 🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
LASER - Language-Agnostic SEntence Representations
stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
word2word - Easy-to-use word-to-word translations for 3,564 language pairs.
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
iSarcasmEval - Datasets used for iSarcasmEval shared-task (Task 6 at SemEval 2022)