Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Routes FlashText tasks for keyword extraction, replacement, fuzzy matching, keyword loading, and keyword-inspection workflows.
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See how to installReview the instructions the agent will receive before you install.
Use this skill when a request names FlashText or asks to extract, replace,
load, inspect, or fuzzily match keywords with KeywordProcessor.
This repository is small enough that one router covers the whole package. Read the bundled references for exact API details instead of reopening the original checkout.
python -m pip install flashtextpython -m pip install -e .python scripts/check_install.pypython scripts/check_install.py --jsonpython -I -c "from flashtext import KeywordProcessor; print(KeywordProcessor().__class__.__name__)"If you are checking whether this skill still matches the repository snapshot,
read references/repo-provenance.md before making that call.
len, in, [], get_keyword, get_all_keywords)? Read
references/api-reference.md and references/workflows.md.case_sensitive, or fuzzy
max_cost behavior? Start with references/workflows.md, then open
references/api-reference.md for exact signatures and return shapes.references/data-formats.md.replace_keywords with tuple clean names, or missed matches caused by word
boundaries or case handling? Read references/troubleshooting.md, then rerun
scripts/check_install.py.references/repo-provenance.md.references/workflows.md for step-by-step usage patterns.references/api-reference.md when you need the exact signature or return
type for a method on KeywordProcessor.references/data-formats.md when shaping file, list, or dictionary
inputs.references/troubleshooting.md for predictable input and matching
failures.scripts/check_install.py whenever you need a quick install/import/
smoke verification.Keep runtime instructions self-contained. Use the bundled references and script instead of the original repository when you need API details, data-shape reminders, or a fast validation check.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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