gautierdag

import torch
import pytorch_lightning as pl

class MyTransformer(pl.LightningModule):
    def __init__(
        self,
        learning_rate=0.001,
        warmup=4000,
    ):
      self.learning_rate = learning_rate

j-adamczyk

import faiss
import numpy as np


class FaissKMeans:
    def __init__(self, n_clusters=8, n_init=10, max_iter=300):
        self.n_clusters = n_clusters
        self.n_init = n_init
        self.max_iter = max_iter
        self.kmeans = None

subhadarship

import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np


class MyDataset(Dataset):
    def __init__(self):
        x = np.random.rand(1000, 3)  # 1000 3-dim samples
        self.x = [x[i].tolist() for i in range(1000)]
        y = np.random.randint(low=0, high=2, size=(1000,))

SuperShinyEyes

def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor:
    '''Calculate F1 score. Can work with gpu tensors
    
    The original implmentation is written by Michal Haltuf on Kaggle.
    
    Returns
    -------
    torch.Tensor
        `ndim` == 1. 0 <= val <= 1
    

tvst

"""Hack to add per-session state to Streamlit.

Usage
-----

>>> import SessionState
>>>
>>> session_state = SessionState.get(user_name='', favorite_color='black')
>>> session_state.user_name
''

nlothian

from tensorflow.contrib.tensorboard.plugins import projector
import tensorflow as tf
import numpy as np
import os

meta_file = "g2x_metadata.tsv"
output_path = "./projections"

# read embedding file into list and get the size
with open('./ft_model.vec', 'r') as embedding_file:

lorey

from bs4 import BeautifulSoup
from markdown import markdown
import re

def markdown_to_text(markdown_string):
    """ Converts a markdown string to plaintext """

    # md -> html -> text since BeautifulSoup can extract text cleanly
    html = markdown(markdown_string)

napoler

# Generated by Percona Configuration Wizard (http://tools.percona.com/) version REL5-20120208
# Configuration name server-1 generated for Luke at 2014-10-22 09:47:40

[mysql]

# CLIENT #
port                           = 3306
socket                         = /var/lib/mysql/mysql.sock

[mysqld]

mcobzarenco

#!/usr/bin/env python
from __future__ import print_function, division
import argparse
import json
import sys
from gzip import GzipFile


def concat_claims(claims):
    for rel_id, rel_claims in claims.iteritems():

jayswan

>>> import itertools
>>> import string
>>> from elasticsearch import Elasticsearch,helpers
es = Elasticsearch()
>>> # k is a generator expression that produces
... # a series of dictionaries containing test data.
... # The test data are just letter permutations
... # created with itertools.permutations.
... #
... # We then reference k as the iterator that's