Questgen.ai
FARM
| Questgen.ai | FARM | |
|---|---|---|
| 3 | 3 | |
| 874 | 1,727 | |
| - | 0.3% | |
| 6.3 | 0.0 | |
| 5 months ago | 5 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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Questgen.ai
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Yes/No style Question and Answer Generation
I have tried to do some searching for models but there don't seem to be ones that do what I am looking for. The closest I found was Questgen, but it only generated the questions and they, more often than, not did not make sense - especially for the types of questions I was looking to generate.
- [D] How to create a question answering system with a (potentially very large) corpus of text?
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Creating a Wikipedia Question/Answer generator
This library might be of help https://github.com/ramsrigouthamg/Questgen.ai
FARM
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Can someone please explain to me the differences between train, dev and test datasets?
I'm also trying to solve this task in a python notebook (.ipynb) using the FARM framework https://farm.deepset.ai/ and BERT model of huggingface https://huggingface.co/bert-base-uncased
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Fine-Tuning Transformers for NLP
For anyone looking to fine-train transformers with less work, there is the FARM project (https://github.com/deepset-ai/FARM) which has some more or less ready-to-go configurations (classification, question answering, NER, and a couple of others). It's really almost "plug in a csv and run".
By the way, a pet peeve is sentiment detection. It's a useful method, but please be aware that it does not measure "sentiment" in a way that one would normally think, and that what it measure varies strongly across methods (https://www.tandfonline.com/doi/abs/10.1080/19312458.2020.18...).
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Has anyone deployed a BERT like model across multiple tasks (Multi-class, NER, outlier detection)? Seeking advice.
You can use https://github.com/deepset-ai/FARM or https://github.com/nyu-mll/jiant for multitask learning. The second is more general.
What are some alternatives?
fastT5 - ⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.
Giveme5W1H - Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?
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.
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
simpletransformers - Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
kiri - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
kiri - Kiri is a visual tool designed for reviewing schematics and layouts of KiCad projects that are version-controlled with Git.
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
MLH-Quizzet - This is a smart Quiz Generator that generates a dynamic quiz from any uploaded text/PDF document using NLP. This can be used for self-analysis, question paper generation, and evaluation, thus reducing human effort.
tldr-transformers - The "tl;dr" on a few notable transformer papers (pre-2022).