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Stefan Smiljkovic PRO
shtefcs
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Reacted to
singhsidhukuldeep's
post with 👍
about 1 month ago
Post
1942
Are you tired of writing scripts to scrape data from the web? 😓
ScrapeGraphAI is here for you! 🎉
ScrapeGraphAI is an OPEN-SOURCE web scraping Python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, etc.). 🌐📊
Just say which information you want to extract (in human language) and the library will do it for you! 🗣️🚀
It supports GPT, Gemini, and open-source models like Mistral. 🔍
A few things that I could not find in the docs but would be amazing to see 🤞:
- Captcha handling 🔐
- Persistent data output formatting 📁
- Streaming output 📡
- Explanation😂 of the tag line: "ScrapeGraphAI: You Only Scrape Once" What does that even mean? 🤣 Is this YOLO? 🤔
Link: https://github.com/VinciGit00/Scrapegraph-ai
Demo code: https://github.com/amrrs/scrapegraph-code/blob/main/sourcegraph.ipynb
ScrapeGraphAI is here for you! 🎉
ScrapeGraphAI is an OPEN-SOURCE web scraping Python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, etc.). 🌐📊
Just say which information you want to extract (in human language) and the library will do it for you! 🗣️🚀
It supports GPT, Gemini, and open-source models like Mistral. 🔍
A few things that I could not find in the docs but would be amazing to see 🤞:
- Captcha handling 🔐
- Persistent data output formatting 📁
- Streaming output 📡
- Explanation😂 of the tag line: "ScrapeGraphAI: You Only Scrape Once" What does that even mean? 🤣 Is this YOLO? 🤔
Link: https://github.com/VinciGit00/Scrapegraph-ai
Demo code: https://github.com/amrrs/scrapegraph-code/blob/main/sourcegraph.ipynb
- 5 replies
This seems really useful to have on business websites.
Reacted to
Felladrin's
post with 👍❤️🔥
about 1 month ago
Post
2710
MiniSearch is celebrating its 1st birthday! 🎉
Exactly one year ago, I shared the initial version of this side-project on Hugging Face. Since then, there have been numerous changes under the hood. Nowadays it uses [Web-LLM](https://github.com/mlc-ai/web-llm), [Wllama](https://github.com/ngxson/wllama) and [SearXNG](https://github.com/searxng/searxng). I use it daily as my default search engine and have done my best to make it useful. I hope it's interesting for you too!
HF Space: Felladrin/MiniSearch
Embeddable URL: https://felladrin-minisearch.hf.space
Exactly one year ago, I shared the initial version of this side-project on Hugging Face. Since then, there have been numerous changes under the hood. Nowadays it uses [Web-LLM](https://github.com/mlc-ai/web-llm), [Wllama](https://github.com/ngxson/wllama) and [SearXNG](https://github.com/searxng/searxng). I use it daily as my default search engine and have done my best to make it useful. I hope it's interesting for you too!
HF Space: Felladrin/MiniSearch
Embeddable URL: https://felladrin-minisearch.hf.space
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- 1 reply
upvoted
3
articles
about 1 month ago
Article
wHy DoNt YoU jUsT uSe ThE lLaMa ToKeNiZeR??
By
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35Article
Tool Use, Unified
•
64
Article
Introduction to ggml
•
114
Excellent post Florent. Specially the graphic examples you provided.
Stefan Smiljkovic
Founder of https://automatio.ai
Reacted to
fdaudens's
post with 🚀🤗🧠👀🔥
about 1 month ago
Post
3038
The Nobel Prize background for Hopfield and Hinton's work on neural networks is pure gold. It's a masterclass in explaining AI basics.
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics 🤝 AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics 🤝 AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
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- 1 reply
Reacted to
tomaarsen's
post with ❤️🚀🔥
about 1 month ago
Post
6350
📣 Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.
1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as
🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:
1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with
2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as
SentenceTransformer("all-MiniLM-L6-v2", backend="onnx"). Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later 😉🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:
1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with
from_model2vec or with from_distillation where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed.2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
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- 1 reply