Giveme5W1H
FARM
| Giveme5W1H | FARM | |
|---|---|---|
| 1 | 3 | |
| 500 | 1,723 | |
| - | 0.3% | |
| 0.0 | 0.0 | |
| 8 months ago | 4 months ago | |
| HTML | Python | |
| Apache License 2.0 | Apache License 2.0 |
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Giveme5W1H
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Date extraction from text code/API's
https://github.com/fhamborg/Giveme5W1H (if you can get it running, I was unable to, maybe try python <3.8)
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?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
ctparse - Parse natural language time expressions in python
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
duckling - Language, engine, and tooling for expressing, testing, and evaluating composable language rules on input strings.
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.
datefinder - Find dates inside text using Python and get back datetime objects
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
extractnet - A fork of Dragnet that also extract author, headline, date, keywords from context, as well as built in metadata extraction all in one package
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
haxe.io - The home of the Haxe Roundup's (Work in Progress)
tldr-transformers - The "tl;dr" on a few notable transformer papers (pre-2022).