- Open-domain Question Answering (Open QA) - efficiently querying large-scale knowledge base(KB) using natural language.
- Two main approaches:
- Information Retrieval
- Transform question (in natural language) into a valid query(in terms of KB) to get a broad set of candidate answers.
- Perform fine-grained detection on candidate answers.
- Information Retrieval
- Semantic Parsing
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import tensorflow as tf | |
import numpy as np | |
corpus_raw = 'He is the king . The king is royal . She is the royal queen ' | |
# convert to lower case | |
corpus_raw = corpus_raw.lower() | |
words = [] | |
for word in corpus_raw.split(): |
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pip install streamlit | |
pip install spacy | |
python -m spacy download en_core_web_sm | |
python -m spacy download en_core_web_md | |
python -m spacy download de_core_news_sm |
This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.
- 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
- Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding
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""" | |
stable diffusion dreaming | |
creates hypnotic moving videos by smoothly walking randomly through the sample space | |
example way to run this script: | |
$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry | |
to stitch together the images, e.g.: | |
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4 |
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""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory | |
git clone https://github.com/myshell-ai/OpenVoice | |
cd OpenVoice | |
git clone https://huggingface.co/myshell-ai/OpenVoice | |
cp -r OpenVoice/* . | |
pip install whisper pynput pyaudio | |
""" | |
from openai import OpenAI | |
import time |
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import json | |
import os | |
import ollama | |
def query_ollama(prompt, model='openhermes:7b-mistral-v2.5-q6_K', context=''): | |
response = ollama.generate( | |
model=model, | |
prompt=context + prompt) | |
return response['response'].strip() |
Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)
That's it!
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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