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@Praneet9
Praneet9 / dist_train.py
Created February 18, 2022 09:38
Distributed Training Complete Script for the Medium Blog
import torch
import torch.nn as nn
import torch.distributed as dist
import torchvision
import torchvision.transforms as transforms
import argparse
from datetime import datetime
import os
import torch.multiprocessing as mp
@BrunoCodeman
BrunoCodeman / standoff2corenlp.py
Created September 26, 2018 19:24 — forked from thatguysimon/standoff2corenlp.py
A python script to turn annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models
# A python script to turn annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models
# - NER format based on: http://nlp.stanford.edu/software/crf-faq.html#a
# - RE format based on: http://nlp.stanford.edu/software/relationExtractor.html#training
# Usage:
# 1) Install the pycorenlp package
# 2) Run CoreNLP server (change CORENLP_SERVER_ADDRESS if needed)
# 3) Place .ann and .txt files from brat in the location specified in DATA_DIRECTORY
# 4) Run this script
@thatguysimon
thatguysimon / standoff2corenlp.py
Last active October 4, 2023 12:04
A python script to convert annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models
# A python script to turn annotated data in standoff format (brat annotation tool) to the formats expected by Stanford NER and Relation Extractor models
# - NER format based on: http://nlp.stanford.edu/software/crf-faq.html#a
# - RE format based on: http://nlp.stanford.edu/software/relationExtractor.html#training
# Usage:
# 1) Install the pycorenlp package
# 2) Run CoreNLP server (change CORENLP_SERVER_ADDRESS if needed)
# 3) Place .ann and .txt files from brat in the location specified in DATA_DIRECTORY
# 4) Run this script
@devashishd12
devashishd12 / gensim_topic_modeling.ipynb
Last active September 19, 2017 16:47
Tutorial on creating and evaluating topic models with Gensim
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