BERT-QE
gensim
| BERT-QE | gensim | |
|---|---|---|
| 1 | 18 | |
| 48 | 15,256 | |
| - | 0.9% | |
| 0.0 | 7.5 | |
| over 2 years ago | 11 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU Lesser General Public License v3.0 only |
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BERT-QE
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[D] BERT-QE: Contextualized Query Expansion for Document Re-ranking (Research Paper Walkthrough)
⏩ Paper Title: BERT-QE: Contextualized Query Expansion for Document Re-ranking ⏩ Paper: https://arxiv.org/pdf/2009.07258.pdf ⏩ Code: https://github.com/zh-zheng/BERT-QE ⏩ Author: Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, Andrew Yates ⏩ Organisation: University of Chinese Academy of Sciences, Amazon Alexa, Institute of Software, Chinese Academy of Sciences, Max Planck Institute for Informatics
gensim
- Aggregating news from different sources
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Understanding How Dynamic node2vec Works on Streaming Data
This is our optimization problem. Now, we hope that you have an idea of what our goal is. Luckily for us, this is already implemented in a Python module called gensim. Yes, these guys are brilliant in natural language processing and we will make use of it. 🤝
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Topic modeling --- allow multiple topics per statement
Try LDA as implemented in gemsin https://github.com/RaRe-Technologies/gensim
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Is it home bias or is data wrangling for machine learning in python much less intuitive and much more burdensome than in R?
Standout python NLP libraries include Spacy and Gensim, as well as pre-trained model availability in Hugginface. These libraries have widespread use in and support from industry and it shows. Spacy has best-in-class methods for pre-processing text for further applications. Gensim helps you manage your corpus of documents, and contains a lot of different tools for solving a common industry task, topic modeling.
- sentence transformer vector dimensionality reduction to 1
- Where to start for recommendation systems
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GET STARTED WITH TOPIC MODELLING USING GENSIM IN NLP
Here we have to install the gensim library in a jupyter notebook to be able to use it in our project, consider the code below;
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Show HN: I built a site that summarizes articles and PDFs using NLP
Nice work! I wonder if you're going the same challenges that gensim had for being generic in summarization.
For context:
> Despite its general-sounding name, the module will not satisfy the majority of use cases in production and is likely to waste people's time.
https://github.com/RaRe-Technologies/gensim/wiki/Migrating-f...
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[Research] Text summarization using Python, that can run on Android devices?
TextRank will work without any problems. https://radimrehurek.com/gensim/
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Topic modelling with Gensim and SpaCy on startup news
For the topic modelling itself, I am going to use Gensim library by Radim Rehurek, which is very developer friendly and easy to use.
What are some alternatives?
FinBERT-QA - Financial Domain Question Answering with pre-trained BERT Language Model
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
scikit-learn - scikit-learn: machine learning in Python
ranking - Learning to Rank in TensorFlow
MLflow - Open source platform for the machine learning lifecycle
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
tensorflow - An Open Source Machine Learning Framework for Everyone
beir - A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Keras - Deep Learning for humans
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
fuzzywuzzy - Fuzzy String Matching in Python