Text-Summarization-using-NLP
skweak
| Text-Summarization-using-NLP | skweak | |
|---|---|---|
| 2 | 8 | |
| 33 | 909 | |
| - | 0.1% | |
| 0.0 | 6.2 | |
| about 1 year ago | 6 months ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Text-Summarization-using-NLP
skweak
-
Entity Extraction with Predefined List
Thanks for pointing me in the right direction. Seems like there’s a few other approaches with weak supervision: https://github.com/NorskRegnesentral/skweak
-
[P] Programmatic: Powerful Weak Labeling
Code for https://arxiv.org/abs/2104.09683 found: https://github.com/NorskRegnesentral/skweak
-
Show HN: Programmatic – a REPL for creating labeled data
Hi Raza here, one of the other co-founders.
I know that HN likes to nerd out over technical details so thought I’d share a bit more on how we aggregate the noisy labels to clean them up.
At the moment we use the great Skweak [1] open source library to do this. Skweak uses an HMM to infer the most likely unobserved label given the evidence of the votes from each of the labelling functions.
This whole strategy of first training a label model and then training a neural net was pioneered by Snorkel. We’ve used this approach for now but we actually think there are big opportunities for improvement.
We’re working on an end-to-end approach that de-noises the labelling function and trains the model at the same time. So far we’ve seen improvements on the standard benchmarks [2] and are planning to submit to Neurips.
R
[1]: Skweak package: https://github.com/NorskRegnesentral/skweak
-
The hand-picked selection of the best Python libraries released in 2021
skweak.
- Skweak: Weak Supervision for NLP
-
Inevitable Manual Work involved in NLP
For more advanced unsupervised labeling, you should check skweak
-
How to get Training data for NER?
I'm the main developer behind skweak by the way, happy to hear you're interested in our toolkit :-) We do already have a small list of products (see https://github.com/NorskRegnesentral/skweak/blob/main/data/products.json) extracted from DBPedia and Wikidata, but it may not be exactly the type of products you're looking for.
What are some alternatives?
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.
snorkel - A system for quickly generating training data with weak supervision
nlu - 1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
pytextrank - Python implementation of TextRank algorithms ("textgraphs") for phrase extraction
DearPy3D - Dear PyGui 3D Engine (prototyping)
snorkel - A system for quickly generating training data with weak supervision [Moved to: https://github.com/snorkel-team/snorkel]
AugLy - A data augmentations library for audio, image, text, and video.
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
awkward - Manipulate JSON-like data with NumPy-like idioms.
jina - ☁️ Build multimodal AI applications with cloud-native stack
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b