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Tweets


https://t.co/UisdhUvhSc https://t.co/WykaxP5gRD








With:
- 611 datasets you can download in one line of python
- 467 languages covered, 99 with at least 10 datasets
- efficient pre-processing to free you from memory constraints
Try it out at:
https://t.co/A7rmjGSELy https://t.co/c8TETfWfHr



I'm a huge proponent of starting before worrying too much about math. But some of you prefer a different approach. If that's you, take a look at this:
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This time we also added some honorable mentions to our main picks. Did we miss any major one? Give us a shout.
Some highlights on the thread. 👇
https://t.co/fQtoGqIlrm



https://t.co/IbkR1FvVWm #javascript https://t.co/XgSbTF4skW

https://t.co/HNPoE2FDrL #DeepLearning #MachineLearning https://t.co/Fci7QR7lWO








Build quick #diagram #application prototypes for typical use cases: https://t.co/WGefD7G8de










This latest release includes increased support for distributed training and mixed precision, new NumPy frontend, and tools for monitoring and diagnosing performance bottlenecks.
Learn more ↓ https://t.co/xFsz9nvz8G





Run BERT training and inference directly on text inputs: #TFHub lets you preprocess text for its numerous BERT models in just a few lines of code.
Read how ↓ https://t.co/AHIjOq7b4W








Here, we look at a rules-based approach to NER via @spacy_io 's EntityRuler to create a rules-based spaCy NER pipe in an empty spaCy model's pipeline. Next video we use this to make a training set.
Video: https://t.co/1IaKSENeXb https://t.co/HM03BD1cFl












We can train an AI model to find where records match, enriching with geospatial data, and using a subject matter expert to validate training data. https://t.co/1RFau0lBhX















https://t.co/Q8EiyYL2Uz https://t.co/W3QdA1nFl6



🛸 Transformer-based pipelines for SOTA models
⚙️ New training & config system
🧬 Models using any framework
🪐 Manage end-to-end workflows
🔥 New & improved APIs
https://t.co/X2i0XwEbv0








We COMPLETELY REVAMPED the graph view.
• Show arrows
• See tags, attachments, orphans, and more
• Tweak various forces for the best layout for your notes
• Toggle in/out links for local graph
• Keyboard navigation
Download: https://t.co/fvXVH4smwg https://t.co/ijykgZCShk







Introducing TensorFlow Recommenders, an open-source package that makes building, evaluating, and serving recommender models easy. Find recommendations for movies, restaurants, and much more!
Get started → https://t.co/ydGyCZgMxT https://t.co/8u7KYnGSsv







Website: https://t.co/wPRxI55fOl
GitHub: https://t.co/MQV5ix7F7p
Press release: https://t.co/jgyzCAkGec https://t.co/p5qcsNWSVI







📖 New & simpler docs and tutorials
🎤 Dialogue & zero-shot pipelines
⭐️ New encoder-decoder architectures: Bert2GPT2, Roberta2Roberta, Longformer2Roberta, ...
📕 Named outputs: https://t.co/T3WzCt8UmY

https://t.co/YOFwhhjVVk https://t.co/iZIfjRqglO





Enjoy the very requested feature while #diagramming in your browser:
https://t.co/Hhr1xKNfT3
#graph https://t.co/1WWWqcoCAH













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