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import argparse
import sys
from stream2sentence import generate_sentences


def file_or_pipe_input(file_path=None):
    if file_path:
        with open(file_path, "r", encoding="utf-8") as file:
            yield file.read()
    else:

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from typing import Dict, Optional

import torch
import torch.nn as nn
from torchcrf import CRF
from transformers import AutoModel


class TransformerSlidingWindower(nn.Module):
    """Apply model on a strided sliding window

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[[package]]
name = "absl-py"
version = "0.12.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
category = "main"
optional = false
python-versions = "*"

[package.dependencies]
six = "*"

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How to run

• Download this as a tab separated file
• Run python create_papers_csv.py --inp acl2020_accepted_papers.tsv --out out.csv --n-keywords 5

Create the embeddings and projection files

• python embeddings.py ../acl-2020-virtual-conference-sitedata/papers.csv
• python reduce.py ../acl-2020-virtual-conference-sitedata/papers.csv embeddings.torch > ../sitedata_acl2020/papers_projection.json --projection-method [tsne|umap]

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import numpy as np
import gzip
import copy
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader


class_mapping = {
    'Rock': 'Rock',

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import numpy as np
import gzip
import copy
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader


class_mapping = {
    'Rock': 'Rock',

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import os
import requests
from torch.utils.data import Dataset
from silx.io.dictdump import h5todict


def download_file(url, fname):
    resp = requests.get(url, stream=True)
    with open(fname, 'wb') as fd:
        for datum in resp.iter_content():

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{
	"Create Class": {
		"prefix": "cls",
		"body": [
		  "class ${1:MyClass}(${2:object}):",
		  "    def __init__(self, ${3:*args}, ${4:**kwargs}):",
		  "        super(${1:MyClass}, self).__init__(${5:*args}, ${6:**kwargs})"
		],
		"description": "Create Class"
	  },

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import mlflow
import mlflow.pytorch


class MlFlowLogger(object):
    def __init__(self,
                 uri=None,
                 experiment_name=None,
                 model_path='models',
                 **params):

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import mlflow
import mlflow.pytorch


class MlFlowLogger(object):
    def __init__(self,
                 uri=None,
                 experiment_name=None,
                 model_path='models',
                 **params):