Nick Lothian nlothian

## Penn Treebank II Tags.md

      
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                nlothian
                / Penn Treebank II Tags.md
            
            
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              June 11, 2024 19:06
            
              
                Penn Treebank II Tags
              
          
    Taken from https://web.archive.org/web/20130517134339/http://bulba.sdsu.edu/jeanette/thesis/PennTags.html (since the source document now appears to be offline)
Penn Treebank II Tags

Note: This information comes from "Bracketing Guidelines for Treebank II Style Penn Treebank Project" - part of the documentation that comes with the Penn Treebank.
Contents:

Bracket Labels

Clause Level

  
## clip_words.txt
!
""""
#
$
%
&
'
(
)
*

## fasttext_to_tensorboard.py
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:

## matplotlib2base64MD.py
def build_GRAPHS_MD(fig):
    # encode as base64 for markdown display
    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    buf.seek(0)
    b64str = base64.b64encode(buf.read()).decode('ascii')

    plt.close()

    markdown_txt = f"""

## Untitled.ipynb

      
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                nlothian
                / Untitled.ipynb
            
            
              Created
              August 5, 2020 11:13
            
              
                Weird PIL behaviour
              
          
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## imagerecognizer.py
from flask import Flask

from flask import request

import simplejson as json
from decimal import Decimal

import numpy as np
import os
import sys

## fasttext_plus_continous_keras.py
inp = Input(shape=(maxlen,), name="text_input") # featureized text comes in here
x = Embedding(embedding_matrix.shape[0], embed_size, weights=[embedding_matrix], trainable=True)(inp)
x = Dense(some_num_here, activation="relu")(x)

extra_data = Input(shape=(1,), name="extra_data")  # your continous features comes in here

combined = concatenate([x, extra_data])

# maybe some ReLu + Dropout here

## M.2 256GB Samsung 950 EVO
sudo fio --filename=./testfile  --direct=1 --sync=1 --rw=read --bs=4k --numjobs=8 --iodepth=32 --runtime=60 --time_based --group_reporting --size=1G --name=journal-test


journal-test: (groupid=0, jobs=8): err= 0: pid=3092: Thu Jun  2 22:48:23 2016
  read : io=21433MB, bw=365792KB/s, iops=91447, runt= 60001msec
    clat (usec): min=11, max=15999, avg=86.72, stdev=34.31
     lat (usec): min=11, max=15999, avg=86.81, stdev=34.31
    clat percentiles (usec):
     |  1.00th=[   60],  5.00th=[   63], 10.00th=[   66], 20.00th=[   75],
     | 30.00th=[   78], 40.00th=[   80], 50.00th=[   82], 60.00th=[   90],

## Trystack images JSON
{
   "images":[
      {
         "status":"ACTIVE",
         "updated":"2012-02-02T19:11:00Z",
         "name":"oneiric-server-cloudimg-amd64",
         "links":[
            {
               "href":"https://nova-api.trystack.org:9774/v1.1/<tenantId>/images/15",
               "rel":"self"

## gist:3127581
{
    "access": {
        "token": {
            "expires": "2012-04-23T23:54:00",
            "id": "<id>",
            "tenant": {
                "id": "<tenantId>",
                "name": "<username>"
            }
        },
	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:
	def build_GRAPHS_MD(fig):
	# encode as base64 for markdown display
	buf = io.BytesIO()
	fig.savefig(buf, format='png')
	buf.seek(0)
	b64str = base64.b64encode(buf.read()).decode('ascii')

	plt.close()

	markdown_txt = f"""
	from flask import Flask

	from flask import request

	import simplejson as json
	from decimal import Decimal

	import numpy as np
	import os
	import sys
	inp = Input(shape=(maxlen,), name="text_input") # featureized text comes in here
	x = Embedding(embedding_matrix.shape[0], embed_size, weights=[embedding_matrix], trainable=True)(inp)
	x = Dense(some_num_here, activation="relu")(x)

	extra_data = Input(shape=(1,), name="extra_data") # your continous features comes in here

	combined = concatenate([x, extra_data])

	# maybe some ReLu + Dropout here
	sudo fio --filename=./testfile --direct=1 --sync=1 --rw=read --bs=4k --numjobs=8 --iodepth=32 --runtime=60 --time_based --group_reporting --size=1G --name=journal-test


	journal-test: (groupid=0, jobs=8): err= 0: pid=3092: Thu Jun 2 22:48:23 2016
	read : io=21433MB, bw=365792KB/s, iops=91447, runt= 60001msec
	clat (usec): min=11, max=15999, avg=86.72, stdev=34.31
	lat (usec): min=11, max=15999, avg=86.81, stdev=34.31
	clat percentiles (usec):
	\| 1.00th=[ 60], 5.00th=[ 63], 10.00th=[ 66], 20.00th=[ 75],
	\| 30.00th=[ 78], 40.00th=[ 80], 50.00th=[ 82], 60.00th=[ 90],
	{
	"images":[
	{
	"status":"ACTIVE",
	"updated":"2012-02-02T19:11:00Z",
	"name":"oneiric-server-cloudimg-amd64",
	"links":[
	{
	"href":"https://nova-api.trystack.org:9774/v1.1/<tenantId>/images/15",
	"rel":"self"
	{
	"access": {
	"token": {
	"expires": "2012-04-23T23:54:00",
	"id": "<id>",
	"tenant": {
	"id": "<tenantId>",
	"name": "<username>"
	}
	},