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from fasthtml.common import *
from fasthtml.components import *
from fasthtml.components import (
D_cite,
)
import os
import json
from fh_plotly import plotly2fasthtml
from plotly import graph_objects as go
import pandas as pd
import plotly.express as px
from eval_result_figures import all_eval_res_figs
##upsampling validation loss graph
# Data
steps = [5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, 50000,
55000, 60000, 65000, 70000, 75000, 80000, 85000, 90000, 95000, 100000,
105000, 110000, 115000, 120000, 125000, 130000, 135000, 140000, 145000,
150000, 155000, 160000, 165000, 170000, 175000, 180000, 185000, 190000,
195000, 200000, 205000, 210000, 215000, 220000, 225000, 230000, 235000,
240000, 245000, 250000, 255000, 260000, 265000, 270000, 275000, 280000,
285000]
fineweb = [2.911076784, 2.75819993, 2.647450924, 2.588769436, 2.565514088,
2.527409077, 2.506954193, 2.493706465, 2.478432178, 2.476668596,
2.467983723, 2.4843328, 2.439449787, 2.438807011, 2.426213741,
2.459792614, 2.426000357, 2.403300762, 2.399668455, 2.404176235,
2.39425993, 2.406329632, 2.381269455, 2.387765169, 2.373465776,
2.374938488, 2.379047394, 2.370839834, 2.351930141, 2.35353899,
2.356842279, 2.343327522, 2.335564375, 2.337301254, 2.322228193,
2.323165655, 2.309478998, 2.326301336, 2.314562321, 2.280953169,
2.291109324, 2.298957586, 2.282609463, 2.296112061, 2.286119938,
2.279724598, 2.268625498, 2.27303195, 2.254729986, 2.260175228,
2.248331785, 2.235989809, 2.241044521, 2.242579222, 2.216429472,
2.250077009, 2.224117756]
txt360 = [2.589649677, 2.438303471, 2.383416414, 2.337049007, 2.300292492,
2.29057312, 2.285922527, 2.265408278, 2.245058537, 2.24157238,
2.23307991, 2.232925415, 2.226780653, 2.222440243, 2.194804668,
2.210517406, 2.186522722, 2.185292006, 2.184529305, 2.185826778,
2.171881914, 2.182652235, 2.169103146, 2.162784815, 2.165525436,
2.159037828, 2.164080143, 2.143726826, 2.144924164, 2.138051987,
2.138092995, 2.120969057, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None]
# Plot
fig_val = go.Figure()
# Add lines
fig_val.add_trace(go.Scatter(x=steps, y=fineweb, mode='lines', name='FineWeb'))
fig_val.add_trace(go.Scatter(x=steps, y=txt360, mode='lines', name='TxT360'))
# Update layout
fig_val.update_layout(
title='Validation Loss Over Steps: TxT360 vs FineWeb',
xaxis_title='Steps',
yaxis_title='Loss',
legend_title='Models'
)
# Show the plot
validation_loss_graph = fig_val
## lm loss graph
# Load the data from the cleaned dataset
data = pd.read_csv('data/lm_loss_txt360_fineweb.csv') # Replace with your actual file path
# Create the plot
fig_loss = go.Figure()
# Add TxT360 line
fig_loss.add_trace(go.Scatter(x=data['Step'], y=data['TxT360'], mode='lines', name='TxT360'))
# Add FineWeb line
fig_loss.add_trace(go.Scatter(x=data['Step'], y=data['FineWeb'], mode='lines', name='FineWeb'))
# Update layout
fig_loss.update_layout(
title="LM Loss Over Steps: TxT360 vs FineWeb",
xaxis_title="Steps",
yaxis_title="Loss",
legend_title="Models",
)
# Display the graph
lm_loss_graph = fig_loss
data = {
"1-1": [17.410227605477868, 17.446573602753478,17.307221780905284,17.338525603992114,17.08551151136689,16.818363305107052,16.98821894111693, 17.125795647512877,16.573462144306383, 15.4293630385597],
"2-5": [16.11176217183986,16.14852530113782,16.297702171159543,15.960924352297502,16.187802102106698,16.474269837858706,15.936494557783181, 15.780419457145868,15.283018703313582, 14.608379914730168],
"6-10": [15.632757662414805,15.627408549576069,15.948641884223639,15.912187993988933,14.935072408852303,14.944741674400241,14.79960386342691,14.631430892394002, 14.378277745163881,14.118271697056592],
"11-100": [15.446116676532212,15.0055028132117,14.799690714225637,14.822102470001267,14.832038213200583,14.568394784374943,14.435682562274105,14.276477514399625, 14.0611924390084,13.880215644749589],
"101-1000": [16.716943171826703,15.565430373421485,14.935989931859659,14.778913482337416,14.508674264491997,14.690158822673334,14.58651834886038,14.337146941773641,13.9886330091318,13.767106666731275 ],
"1001-30000000": [18.156821563322765,17.314701050452452,16.09585768919658,15.428145290012955, 14.800605964649103, 15.990949424635108,15.869365567783806,15.872474774329305, 15.769421394877273, 15.05749135510839],
}
# Years for the x-axis
years = ["2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"]
# Create a plotly figure
fig11 = go.Figure()
# Add a trace for each bucket
for bucket, values in data.items():
fig11.add_trace(go.Scatter(x=years, y=values, mode='lines', name=bucket))
# Update layout for better presentation
fig11.update_layout(
title='Perplexity Versus Buckets for Different Years',
xaxis_title='Year',
yaxis_title='Perplexity',
xaxis_tickangle=-45,
legend_title="Buckets",
)
Perplexity_Across_Different_Buckets_global_graph = fig11
##graph 2
# Data
data = {
"1-1": [17.410227605477868, 17.446573602753478,17.307221780905284,17.338525603992114,17.08551151136689,16.818363305107052,16.98821894111693, 17.125795647512877,16.573462144306383, 15.4293630385597],
"2-5": [16.11176217183986,16.14852530113782,16.297702171159543,15.960924352297502,16.187802102106698,16.474269837858706,15.936494557783181, 15.780419457145868,15.283018703313582, 14.608379914730168],
"6-10": [15.632757662414805,15.627408549576069,15.948641884223639,15.912187993988933,14.935072408852303,14.944741674400241,14.79960386342691,14.631430892394002, 14.378277745163881,14.118271697056592],
"11-100": [15.446116676532212,15.0055028132117,14.799690714225637,14.822102470001267,14.832038213200583,14.568394784374943,14.435682562274105,14.276477514399625, 14.0611924390084,13.880215644749589],
"101-1000": [16.716943171826703,15.565430373421485,14.935989931859659,14.778913482337416,14.508674264491997,14.690158822673334,14.58651834886038,14.337146941773641,13.9886330091318,13.767106666731275 ],
"1001-30000000": [18.156821563322765,17.314701050452452,16.09585768919658,15.428145290012955, 14.800605964649103, 15.990949424635108,15.869365567783806,15.872474774329305, 15.769421394877273, 15.05749135510839],
}
# Create a line plot for each bucket
# Years
years = ["2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"]
# Create the figure
fig22 = go.Figure()
# Add lines for each bucket
for bucket, perplexities in data.items():
fig22.add_trace(go.Scatter(x=years, y=perplexities, mode='lines+markers', name=bucket))
# Update layout
fig22.update_layout(
title="Perplexity Across Different Years",
xaxis_title="Year",
yaxis_title="Average Perplexity",
legend_title="Bucket (duplicate count range)"
)
# Show the figure
graph2222 = fig22
#graph 3 tbd
data = {
"2014": {
"x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300],
"y": [17.410227605477868, 16.28263447660928, 16.056605184079913, 15.822614148687798, 15.842634843649147, 15.720699070243016, 15.687223294726039, 15.607834732758393, 15.572091951204342, 15.475459290252376, 15.337665345612953, 15.33475421632639, 15.325861061422275, 15.476636737344972, 15.693804028233785, 15.689754199668954, 15.659926393713182, 15.59464057615171, 15.588725895678929, 15.552308157234252, 15.38315488135992, 15.3749391039699, 15.186437103290823, 15.179577016115116, 15.175359268463414, 15.326051392123386, 15.550365066676973, 15.46274973581214, 15.675382592723022, 15.6083647300616, 15.775496160899257, 15.619470488458068, 15.630267399750903, 15.687059334218864, 15.415897825337026, 15.499602281425519, 15.598025850718154, 15.628460503669615, 15.658570343618969, 15.787877514096849, 15.710944180881294, 15.552274945554545, 15.699932540053279, 15.727560356232173, 15.599824119717585, 15.683275006354283, 15.641738631258287, 15.80315709908002, 15.706620536337898, 15.700588878168745, 15.911432681730176, 15.550009275425229, 15.822384367996918, 16.105816904042662, 15.562383488563118, 15.963855835984134, 15.852621368738493, 15.941133570933058, 16.188290366801112, 15.70084365155994, 15.609990413956307, 15.673631920556218, 16.006611785209323, 15.786230274505419, 15.433452154301659, 15.617135397912477, 15.640107157600301, 15.675806627110681, 15.671948319822476, 16.068440880032057, 15.769336862872192, 16.40254172818583, 15.940476853009686, 16.09877983788286, 15.495609182160944, 15.712818514069026, 15.61898546436921, 15.692158946904128, 16.493172099511007, 15.783765491247193, 16.055784648916596, 16.334082795006267, 16.176010490897976, 15.630577127947536, 15.905029905131835, 16.610640775886303, 15.245514025399112, 16.28252696514428, 15.435644188406563, 15.888062835224325, 16.250276704907034, 16.054194778686796, 16.620639801236162, 16.102213595669074, 16.09682778567417, 15.881305605266526, 16.217666050845356, 15.886905671039385, 16.420120825444208, 16.575208311709837, 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},
"2023": {
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"y": [15.4293630385597, 14.827776633211421, 14.600432832118155, 14.43330043760322, 14.30586483681026, 14.203397641081045, 14.140361413924607, 14.101673126860582, 14.05840021694595, 14.033693337279875, 13.990003714671388, 14.001106927608756, 13.982387676044238, 13.960424890216352, 13.941694305252629, 13.928958405693843, 13.922871327026984, 13.90749356497257, 13.883187320364065, 13.870538613453949, 13.853682922141118, 13.839326154723096, 13.841949693311191, 13.851284862386178, 13.853606323578846, 13.851840301257587, 13.86166226046842, 13.872513892742713, 13.867295530090015, 13.870385212514083, 13.868578068850889, 13.848685425568009, 13.838736750620761, 13.825182992628129, 13.795340100698754, 13.809629502148145, 13.767911089744738, 13.75599639262174, 13.75925151191706, 13.751975433642748, 13.731931931502134, 13.714316407794309, 13.730848265421725, 13.681606796845, 13.670846152397202, 13.685040324991581, 13.687292733648798, 13.645627677729081, 13.635021438002346, 13.670103747374988, 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},
}
fig33 = go.Figure()
for year, year_data in data.items():
fig33.add_trace(go.Scatter(
x=year_data['x'],
y=year_data['y'],
mode='lines+markers',
name=f"Year {year}",
line_shape='linear'
))
# Update layout
fig33.update_layout(
title="Perplexity vs. Number of Duplicate Documents Over Years",
xaxis_title="Number of Duplicate Documents",
yaxis_title="Average Perplexity",
legend_title="Year",
hovermode="x unified"
)
graph3 = fig33
##graph 4
# Data
data = {
"2014": [[1, 2, 3, 4, 5, 6, 7], [16.82572561037536, 15.568688184472002, 15.81492345088457, 15.206355227978303, 14.957766124315622, 15.0626618458479, 14.868694970954875]],
"2015": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], [16.89591942700286, 16.065453332424525, 16.27127382267357, 15.98729389349345, 15.820859416672139, 15.607600911192797, 15.110138119917048, 14.775887397593593, 14.660655022362318, 14.588547400309, 14.55020529201701, 14.60127048285861, 14.629467286160864, 14.724440862889]],
"2016": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], [16.937063110995666, 16.159401045667217, 16.24178377816029, 16.073494730595225, 15.774737597940442, 15.621947075605162, 15.652955728810767, 15.490807185265608, 15.107547930881259, 14.976802818244652, 14.84517846815987, 14.839768640933237, 14.794471716989547, 14.63826024444852, 14.57725826735028, 14.446983381957654, 14.357863327746747, 14.370146480850329, 14.207796791759758, 14.067617540013867, 13.968037234662484, 14.015386030957798, 14.240341030736081]],
"2017": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], [16.731010662045737, 16.534529481773635, 16.624818021435388, 16.6027968727952, 16.309053610063657, 15.897351021398148, 15.28125612085656, 15.247650153402024, 15.254523671401472, 14.914419299947483, 14.734687782223004, 14.78413990847839, 14.632065773173599, 14.553246204352645, 14.566566717229817, 14.598997883895558, 14.630837564582592, 14.577208610859856, 14.519413074334954, 14.378498447103155, 14.230943065441672, 14.21486380216727, 14.267422050116286, 14.281606945252474, 14.285535565035358, 14.267266452303197, 14.183136371592417, 14.148268300539986, 14.21036055571916, 14.403658436211263, 14.07740471177247, 13.978242496152737, 14.002633023111342, 13.921442192327655]],
"2018": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46], [16.36546565307699, 16.40198820766892, 16.223802437472045, 15.876190858008776, 15.67866795074444, 15.06202559354935, 14.90395971116697, 14.678203166785968, 14.584307457124408, 14.566603376547835, 14.716527825876883, 15.325114798824192, 15.153635738494962, 15.02749650458142, 15.001501640976104, 14.928437622685198, 14.82066263298361, 14.821925418558614, 14.766538823073754, 14.742836908980854, 14.56365471694568, 14.676889493065413, 14.554327268897188, 14.532997117657752, 14.424665661602356, 14.498427111442304, 14.503622356394782, 14.465294436509943, 14.238209588803269, 14.138531026657803, 14.11633073136879, 14.139167312709546, 14.018054330979824, 13.99555243574117, 13.944770385859002, 13.893353445566834, 14.062344563726617, 13.93602183204734, 14.021518977400955, 14.205273479299835, 14.476114932821364, 14.707612238290547, 14.403724822460271, 14.134464626849528, 14.444143421676507, 14.372513155481114]],
"2019": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58], [16.430122616908218, 16.959894024725404, 17.530494343918114, 16.868158683774006, 15.881925908038214, 15.534071764082064, 15.131599277920825, 14.912091189771328, 14.670101527122705, 14.591504400695127, 14.405999306200146, 14.445437111619501, 14.460150974610382, 14.484377024024335, 14.454923933668082, 14.419367200283267, 14.413528998792588, 14.362242243379736, 14.326113076490996, 14.296366861932851, 14.241137636937816, 14.199018730937293, 14.229939247452181, 14.136900232594229, 14.118747592622253, 14.084639441555339, 14.215359965064522, 14.321335291582407, 14.324483466296755, 14.22250351319463, 14.086153725088337, 13.835546707594524, 13.861133395338289, 13.943395906095482, 13.924621051048792, 13.978711084778347, 13.905837106100599, 13.895058375110484, 13.965442215811176, 13.960110799963521, 13.99641930380663, 14.036529641608142, 14.138294825947872, 14.3360579115842, 14.381214824082278, 14.389155929858507, 14.645175007732961, 14.477402464547152, 14.51444505845359, 14.452153618691407, 14.369430039316658, 14.777857326947053, 14.919588044831785, 14.44053007434645, 15.18596756335163, 15.362545102466955, 14.19095014510728, 14.974116009038374]],
"2020": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67], [16.370879996210345, 15.8912555858172, 15.30157381561316, 15.461362783916728, 15.499602726814674, 15.329200065422526, 15.15238010628145, 14.763158730707703, 14.554444724050978, 14.391542531017906, 14.36062417910266, 14.38408773153761, 14.326398992080678, 14.30559398460406, 14.296620966953196, 14.257641523080219, 14.239785079623704, 14.257963344315925, 14.242187947481078, 14.227247960684823, 14.246528806504662, 14.218691858857001, 14.119027315863725, 14.16049126937658, 14.1393355330565, 14.045446781499932, 14.157717833319223, 14.118844952908322, 14.196031529646438, 14.213151244097208, 13.961708881578405, 13.933434907040512, 13.851967361230258, 13.837032186918211, 13.83609308408103, 13.74084041793037, 13.725470779946555, 13.828238575588477, 13.821753201986834, 13.81152543072784, 13.848104780631868, 13.867032605563987, 13.91567734841802, 13.951714207206178, 14.049401288158215, 14.000020142532282, 14.097054408814992, 13.973915015915077, 14.001305792897133, 14.144447551433583, 14.259794771973013, 14.451580305620148, 14.51523695971965, 14.491781843544896, 14.282130970472108, 15.107471010795585, 14.507809743534436, 14.521069179252333, 14.434674191986367, 14.608533166251355, 14.638444470251414, 14.533496524040576, 14.798278071146985, 14.426231926251468, 14.697215649677831, 16.000970311873413, 15.962112261396376]],
"2021": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76], [16.40998076014569, 16.214064988829055, 16.102423371338, 15.61960664445389, 15.365806380257993, 15.258505702187641, 14.987691202287733, 14.702934989974425, 14.47618699872759, 14.27294780183996, 14.149902488372312, 14.205153888507418, 14.180406523176302, 14.179162306409303, 14.148172641684653, 14.146789562089962, 14.13207546325995, 14.095045629570848, 14.11059784727951, 14.099774964675055, 14.123404475264994, 14.04205645064162, 14.071957340879374, 14.076002230293538, 13.997579723886533, 14.055982816834012, 13.969566421026498, 14.044197137615983, 13.970800321234117, 13.991254850488748, 13.882773884993162, 13.878676595695538, 13.738534890032913, 13.731264526843741, 13.743931319054433, 13.744385448034985, 13.654269807446708, 13.649793785589242, 13.659669286701138, 13.70981039304929, 13.706017327034093, 13.793638036031938, 13.820253019972634, 13.895844222751752, 13.805532005211788, 13.879969410141696, 13.918667434527839, 13.905080860878972, 13.928649229766597, 13.92971106121914, 14.027211058825408, 13.869387194129342, 14.002439547556614, 14.197189244986761, 14.171599593405029, 14.189026952927275, 14.317088413682809, 14.266884102594418, 14.419819049388389, 14.394102801112203, 14.28486572885178, 14.463889605992794, 14.33813105749612, 14.49991185507524, 14.143521529230846, 14.020378845184027, 13.929518285869944, 14.785566241242854, 14.273112202537183, 14.214773231874327, 15.148250280334482, 14.563912283938823, 14.552655401047055, 15.103655560583926, 15.10169368308145, 15.51226670578053]],
"2022": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82], [15.816438075969113, 16.09580666529207, 15.996333190110686, 15.943848815283268, 15.656093894572393, 15.117394092950262, 14.78287160043243, 14.543141634926663, 14.305058018637094, 14.125611753367533, 14.020067247129745, 14.009478965058692, 14.001669618595649, 13.985248005578073, 13.973728782674742, 13.977117237954532, 13.937202607945661, 13.935625722728988, 13.910945770197134, 13.893835251097759, 13.89679037463924, 13.881064193374637, 13.904299030382344, 13.904090626213797, 13.888526913768459, 13.838568567514612, 13.829178463025315, 13.835043064902585, 13.81757570730985, 13.776370281729609, 13.762638676519028, 13.755074572847866, 13.70045870380896, 13.636805800875507, 13.621671132216942, 13.633589926779942, 13.602080944483301, 13.555848610415586, 13.558904660155596, 13.515784046498322, 13.609062507319349, 13.583137427458137, 13.483909540769114, 13.552547586090368, 13.598431457952788, 13.553603210491028, 13.57678073518515, 13.638974685844074, 13.536340332636364, 13.665029112653803, 13.792584632059397, 13.694618901117751, 13.76826442085512, 13.712141215124436, 13.606927085734855, 13.715251358141709, 13.832372298562165, 13.833057817510603, 14.158282794215733, 13.720855464980609, 13.803417413649743, 13.969854384335488, 13.892171370159991, 13.778269401162506, 14.338216122354686, 13.954993084366778, 14.124621205791534, 14.23737489353975, 13.735373769168353, 14.680642990647039, 14.955805632680471, 14.657580957949671, 14.515390831490835, 14.664661248141265, 14.497750142907295, 14.662641057721332, 14.889760996859867, 14.044358215057608, 14.201835355776206, 15.739648390380614, 16.209835613545337, 16.152241961523004]],
"2023": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87], [14.789164834058647, 14.816940361326127, 14.859128791010018, 14.874353138456074, 14.799558338831533, 14.521955875294703, 14.344439116861626, 14.188768591243738, 14.068214649484007, 13.975247139727212, 13.892361806680233, 13.873812682757855, 13.855623423924795, 13.834034581531931, 13.820704041290954, 13.809345726828388, 13.799809305012891, 13.783733425983497, 13.777650159371621, 13.769295261894937, 13.759922637457876, 13.763157283741176, 13.746892077566258, 13.76043892787152, 13.780799936451544, 13.769219249583701, 13.776107653632701, 13.791395904208231, 13.796017873163873, 13.793511195440795, 13.752832872061946, 13.744140909741626, 13.708291014183953, 13.71791539682566, 13.687888739966342, 13.639779981608498, 13.60820604942688, 13.647619221630219, 13.586732110007599, 13.578824230397977, 13.56286775189147, 13.54887132723798, 13.528005768240323, 13.562278263274335, 13.50423334091739, 13.534814940452101, 13.500524731734556, 13.50415611423685, 13.539792624454682, 13.524280636462276, 13.508635254011224, 13.559898859515048, 13.52340866000508, 13.580987242413414, 13.55406078682323, 13.51949982056399, 13.554946096777044, 13.582384634669271, 13.596405485832927, 13.56397157046417, 13.50076890156882, 13.5996552161972, 13.640177272640555, 13.488306430506505, 13.561144538501607, 13.538229936728902, 13.523274401746107, 13.549803559768709, 13.473822808409837, 13.604196078834873, 13.495970010370977, 13.511897788189435, 13.480753572940852, 13.790758773339501, 13.507154643917646, 13.451282820788672, 13.690887252346345, 13.752272760476266, 13.844969190718858, 13.547043033265561, 14.011397551896307, 13.686917203544905, 13.87527368747695, 13.86828531076653, 14.200910595590095, 14.228639023300655, 14.830844952562549]],
}
# Create figure
fig = go.Figure()
# Add a line for each year
for year, values in data.items():
x_values = values[0] # Dumps duplication count
y_values = values[1] # Average perplexity
fig.add_trace(go.Scatter(
x=x_values,
y=y_values,
mode='lines+markers',
name=year
))
# Update layout
fig.update_layout(
title="Perplexity Across Different Dump Duplication Counts",
xaxis_title="Number of Dumps Duplication",
yaxis_title="Average Perplexity",
legend_title="Year"
)
# Show figure
graph4 = fig
#graph 5
# Data
data = {
"2014": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.993513389984585, 14.011578085529056, 13.85873136108517, 14.36377987125267, 15.699475274542996, 17.613998844460845]],
"2015": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.471687658163091, 13.778144432139904, 14.021706888859466, 14.434268636964038, 15.738483406741972, 17.840661656870072]],
"2016": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.438546004899326, 13.785269267918505, 14.181493168285378, 14.616607805125264, 16.2678738383595, 18.109357061988614]],
"2017": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.567324655164636, 13.93914285288248, 14.443292405833132, 14.749691886458645, 17.212768319133406, 18.09921482533539]],
"2018": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.79413309579284, 14.047920703385312, 14.732393204862687, 15.245556746489475, 17.523092460922793, 17.979494192145445]],
"2019": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.755894620552562, 14.061328527771183, 14.921709511248036, 15.746644701243795, 17.6501867538675, 18.65346532837317]],
"2020": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.808975955128705, 14.075864850752456, 14.857958945409392, 15.491042607097183, 17.423084409503307, 17.519155049505677]],
"2021": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.97373049489925, 14.044277002343486, 14.943719820749207, 15.730257843247038, 17.289265921502192, 17.00252056705296]],
"2022": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.865869368871065, 14.028544413850119, 14.920117888974543, 15.693608290158098, 17.33525765189499, 17.724085896005107]],
"2023": [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [14.499660832882716, 13.98118612654411, 14.86417535071986, 15.440738449977292, 16.94447963146979, 16.40768745134217]]
}
# Create figure
fig = go.Figure()
# Add a line for each year
for year, values in data.items():
x_values = values[0] # Buckets (duplicate count range)
y_values = values[1] # Average perplexity
fig.add_trace(go.Scatter(
x=x_values,
y=y_values,
mode='lines+markers',
name=year
))
# Update layout
fig.update_layout(
title="Perplexity Across Different Buckets",
xaxis_title="Bucket (Duplicate Count Range)",
yaxis_title="Average Perplexity",
legend_title="Year"
)
# Show figure
graph5 = fig
#graph 6
import plotly.graph_objects as go
# Data
data = {
"2014": {
"x": list(range(1, 88)),
"y": [16.82572561037536, 15.5729544573954, 15.76916326514348, 15.186302038671778, 14.936809838870326,
15.001743417469209, 14.864349796797914, 14.66090559477578, 14.50521188055062, 14.429989457983405,
14.44107090883399, 14.523867649934015, 14.515728033558386, 14.52893012438083, 14.3872701582485,
14.225935934612775, 14.161270385625249, 14.193552536884559, 14.107559093534066, 14.045872060793387,
13.98606116940174, 13.996535021107574, 14.068451357323962, 14.095424742580935, 14.104930415857556,
14.164512298046297, 14.205441475464172, 14.190478352923273, 14.080583365838212, 14.017757335246573,
13.914391577020234, 13.860680803527211, 13.709718483172745, 13.671102868173037, 13.641967377585344,
13.610477152664181, 13.589404166531697, 13.574364349432603, 13.588675304139612, 13.608431855502594,
13.602022539289871, 13.577550630742646, 13.5388901112764, 13.586266218232485, 13.543364159175262,
13.530598188250648, 13.5415846166861, 13.518331785106323, 13.533012163841759, 13.505063315507076,
13.546433152010087, 13.571041150558058, 13.538517630502037, 13.589716598763259, 13.542375131110317,
13.551229472032986, 13.577615792486487, 13.580265758570304, 13.611791663131656, 13.563252675321158,
13.496554744217198, 13.573611486889268, 13.619417346323402, 13.500110681053787, 13.606494277444822,
13.51758492586705, 13.509290876196683, 13.637653184648098, 13.453927168439604, 13.677712744726351,
13.586150504785195, 13.508649309624724, 13.427111951862848, 13.800940000499685, 13.540635679680195,
13.506572883300588, 13.723957125925423, 13.69941910754067, 13.830916622405972, 13.61477646698581,
14.050670659742897, 13.707871486489617, 13.879222794014064, 13.838270894748867, 14.189041030733511,
14.221410654489386, 14.830844952562549]
},
"2015": {
"x": list(range(1, 88)),
"y": [16.89591942700286, 15.993069023056673, 16.243349252734195, 16.110419584106467, 15.982982023217136,
15.780412452462956, 15.311492684065183, 14.985452841100962, 14.736003632647204, 14.643740634573437,
14.581818722981641, 14.61206887509358, 14.60310477647637, 14.58283877743656, 14.456739625003323,
14.371166857791525, 14.31983561927552, 14.324744175103753, 14.230377570291408, 14.172400195643684,
14.074979303784101, 14.06637978929149, 14.129765267158923, 14.144182701579643, 14.127393749297303,
14.170954937339665, 14.198905272060887, 14.177619224859075, 14.073073703918407, 14.014468948454157,
13.91281662387792, 13.866047771993978, 13.718029097387152, 13.680243640703553, 13.648583428855366,
13.620824659009545, 13.599252706541842, 13.582576892167687, 13.59570512552313, 13.620006512175056,
13.61371460828329, 13.58747315850088, 13.553159834309662, 13.595855683037627, 13.555194351409998,
13.545131292437988, 13.55533031134806, 13.53176578053929, 13.545627399503374, 13.52324264159441,
13.56019390924936, 13.584765148382376, 13.567436274268637, 13.613633832001781, 13.56834903785532,
13.564886395063189, 13.585588042180726, 13.59211972866627, 13.636661940057897, 13.583630103422175,
13.517621040928827, 13.621723155241801, 13.644527863238189, 13.52150990576411, 13.63020958334669,
13.54428783928786, 13.53725206048877, 13.647484409192606, 13.490519893023992, 13.683557383784123,
13.623112173204985, 13.558759376238477, 13.498783050518579, 13.848023903419993, 13.565373956282203,
13.524041036650617, 13.747948319462374, 13.741843984167335, 13.851135619804712, 13.592769333513887,
14.059753132138649, 13.714031759165366, 13.87123035917049, 13.863386012094978, 14.205561271109257,
14.22054632648026, 14.830844952562549]
},
"2016": {
"x": list(range(1, 88)),
"y": [16.937063110995666, 16.13008897039131, 16.461262248281052, 16.22524701869722, 16.07512945589454, 15.81169518738005, 15.401255747943388, 15.290536877276061, 15.189318784104037, 14.892680148096144, 14.874941231589043, 15.197590171491255, 14.874108876718813, 14.772934695761471, 14.717224961147101, 14.579633089306332, 14.50655782645537, 14.435927961362305, 14.348044387963261, 14.266921166623963, 14.175146176030633, 14.155440421923364, 14.193605317227636, 14.198019720781069, 14.163648287310021, 14.204946332399512, 14.22475496545738, 14.204790263028082, 14.093780256147781, 14.034401436018054, 13.932675009733511, 13.882052276281941, 13.74135284070189, 13.70495830877455, 13.67945861250605, 13.646286996486673, 13.62871684949669, 13.617194154766194, 13.625737566495989, 13.647397208854517, 13.644435990272989, 13.61908161116556, 13.579719730143186, 13.62432154955091, 13.586802920088218, 13.575059311720242, 13.583160915709527, 13.560181345211744, 13.568315314106055, 13.572213203030019, 13.592269857254504, 13.615523084934742, 13.59675061839986, 13.652762841246345, 13.605972800137922, 13.599797687151108, 13.61588443406148, 13.635017100727426, 13.67797680326676, 13.61550407767841, 13.549525029810649, 13.646426348264216, 13.685855644417883, 13.557730732623456, 13.654494917893674, 13.598966492740331, 13.596850824269557, 13.67606211623268, 13.531829321066132, 13.736041595486006, 13.66155540112554, 13.622341728732476, 13.576672515819212, 13.865689693447742, 13.593823653503895, 13.549026603238106, 13.757031791888123, 13.764537976595522, 13.887154797262793, 13.612757682949674, 14.062295065487943, 13.702773103249053, 13.887824206090368, 13.868971352456365, 14.206954791830077, 14.23126212995893, 14.830844952562549],
},
"2017": {
"x": list(range(1, 88)),
"y": [16.731010662045737, 16.66489684871436, 16.568296606954515, 16.437855314705402, 15.977926296110395, 15.325063337637374, 14.928282696800443, 14.738959466164316, 14.615199372327732, 14.491701731769748, 14.38837234689295, 14.579509085357541, 14.486096782010588, 14.386514742964506, 14.328783257345318, 14.247944995447972, 14.19065895107961, 14.17354382678591, 14.151039883117742, 14.102188877847754, 14.076004337973803, 14.044354444990084, 14.021645356806046, 14.030621721602873, 14.020477810045843, 14.031701258052667, 14.04171400712978, 14.043074023094446, 13.995387210524175, 13.989640260692584, 13.926046401793304, 13.898066232753337, 13.796470656270547, 13.780012350603617, 13.751151287426516, 13.721059118625764, 13.704432047525122, 13.69302750426319, 13.691196863860615, 13.720022791468368, 13.713297481354758, 13.673499420782665, 13.640347860637759, 13.689388371843485, 13.642074890212623, 13.641953443289871, 13.639384501232673, 13.60468612819505, 13.615030447011026, 13.622517997479935, 13.636776517056651, 13.646713543499644, 13.642553869063399, 13.670086160771016, 13.629072087229995, 13.644023633211804, 13.662921343294622, 13.663979848619121, 13.72041962705924, 13.653883587863746, 13.598487708047587, 13.679216506372514, 13.729500926935524, 13.59184630155257, 13.670119193506638, 13.606578039356629, 13.58912160797548, 13.662329284657567, 13.534494438724048, 13.673846450840824, 13.63996498360781, 13.62694917771645, 13.569375400530905, 13.86109411358942, 13.573009950696505, 13.521398904176095, 13.737765136250907, 13.73947064730135, 13.851957566097216, 13.591944403455463, 14.047275913134344, 13.706497861963147, 13.8752419700006, 13.852410962776265, 14.207173465915606, 14.225732066062445, 14.830844952562549],
},
"2018": {
"x": list(range(1, 88)),
"y": [16.36546565307699, 16.45401827941957, 16.3552192563823, 16.073789764186234, 15.71971918795885, 15.261114192297178, 14.951234486665056, 14.689367076535861, 14.518128318670973, 14.390099476525092, 14.259121880707934, 14.322341013577667, 14.269149775349785, 14.22657504335791, 14.17831063712821, 14.129362751026134, 14.091233660298563, 14.065699851355896, 14.045897986269743, 14.009922933506038, 14.008946008943926, 13.986948087868985, 13.96676299568112, 13.974589147811011, 13.981182540647008, 13.988178680995725, 14.022037638217835, 14.067236496270958, 14.02773875655739, 14.008012013219155, 13.96047327707988, 13.941545910212922, 13.874283503971592, 13.872319571354534, 13.848819496944696, 13.818989807687052, 13.795278583261243, 13.805582941066566, 13.77608884517368, 13.808300541084458, 13.801215406173135, 13.77460925841957, 13.749962347541663, 13.788585819175475, 13.738353323889454, 13.748267848539136, 13.743909070082335, 13.70094253905992, 13.695566142077064, 13.715120021384273, 13.717879111446795, 13.729262136679193, 13.722322759559463, 13.741817617080867, 13.695797507509411, 13.7303223115229, 13.721142524694981, 13.731183807899438, 13.774251666658795, 13.703270226348065, 13.650013779539329, 13.737063911330598, 13.771299601507252, 13.61035413656345, 13.692911759706188, 13.645478213049078, 13.632427440948039, 13.685353855181187, 13.536951397436473, 13.722996576069109, 13.684668668827719, 13.654991978676541, 13.571293959475197, 13.901327051067131, 13.590409846183357, 13.555280671785631, 13.801358266402337, 13.759891436212222, 13.877447278904341, 13.601070277589484, 14.053793686670241, 13.74239252938787, 13.856380061979658, 13.86653563573893, 14.207967017152274, 14.2295264543635, 14.830844952562549],
},
"2019": {
"x": list(range(1, 88)),
"y": [16.430122616908218, 16.875051086326838, 17.248145032061572, 16.40581365808372, 15.774430714575988, 15.432767149001718, 15.145763219926579, 14.799558762994192, 14.497109888060386, 14.314004689580893, 14.165230875628717, 14.132599998200735, 14.112646717547443, 14.08981745552461, 14.071275351524006, 14.055953745980803, 14.036895611705631, 14.010822458772108, 13.994688510028434, 13.981568297524671, 13.973131490690857, 13.960851307613188, 13.950407299676206, 13.957766631077842, 13.963923860192619, 13.94968503524683, 13.95489134897448, 13.977622152992309, 13.976393846039631, 13.993956516762394, 13.953670292574621, 13.955101324894523, 13.921696126960061, 13.922513268517232, 13.904228567859588, 13.878944392338122, 13.836936389689685, 13.858488953034747, 13.826255387319108, 13.821733497321453, 13.810924551284534, 13.788436757401502, 13.768651883781944, 13.803381095808694, 13.749861429185598, 13.766023951467982, 13.755033569040371, 13.733275547786105, 13.72346431637034, 13.742887974648536, 13.737327971523658, 13.755247742270127, 13.742754785888708, 13.774345891745789, 13.728724104358966, 13.745302135799491, 13.734638471314582, 13.7609514564792, 13.779567132297817, 13.70364460650017, 13.66329614928825, 13.77103900712268, 13.794109949823657, 13.621080526861675, 13.685285026135551, 13.645675824035878, 13.640254544445797, 13.696632265084162, 13.557089062423215, 13.72744068935533, 13.685312250371716, 13.691487365024885, 13.609612243178898, 13.871590990738959, 13.602896495117179, 13.545298074232608, 13.754105324744064, 13.784568829720575, 13.848250131622855, 13.601751414645124, 13.999031818997702, 13.692806805453536, 13.82245603247244, 13.856124808569652, 14.187925457414872, 14.223944149123374, 14.830844952562549],
},
"2020": {
"x": list(range(1, 88)),
"y": [16.370879996210345, 15.889055037950405, 15.432190412594073, 15.505691782112706, 15.423788890368417, 15.240064903157505, 15.043391085766707, 14.761714938396674, 14.47962007552951, 14.234133021817758, 14.123336425923434, 14.080419066871459, 14.063393745363433, 14.051331002053393, 14.037897577554489, 14.041538161067855, 14.0145622985008, 13.995935362579743, 13.991791105247946, 13.983842732394558, 13.977020615720546, 13.966291155429351, 13.953143025987027, 13.969577694685832, 13.972274556692618, 13.967416689282992, 13.962867288546066, 13.983570820646507, 13.983105305183239, 14.007290834079699, 13.960017748248399, 13.97014807600787, 13.939131974855112, 13.931242142373641, 13.923784747364268, 13.884450633737517, 13.857702881178751, 13.86969533995838, 13.829427121984313, 13.834994065394055, 13.821808671828476, 13.790151002027123, 13.76907986225701, 13.797444154897008, 13.749256987787135, 13.766070221096518, 13.759434645334379, 13.731706353193822, 13.733304557490518, 13.740468199068971, 13.721036580994003, 13.756529386584269, 13.732150648873972, 13.763427764616013, 13.73375582951499, 13.719098308155836, 13.749579407257558, 13.79960240262951, 13.827660100575278, 13.713867309731741, 13.67110069190749, 13.813210464397173, 13.820210906230622, 13.646742569819427, 13.707821135028649, 13.694993324955234, 13.667868455414132, 13.744045961641467, 13.60673607296966, 13.735891876117867, 13.708771958501817, 13.68966975607113, 13.632689223663043, 13.935886970943438, 13.636130655693483, 13.612174548563797, 13.772534936472159, 13.807161877232101, 13.85457230058511, 13.663896751660037, 14.090271528268559, 13.73950346577504, 13.889399028025398, 13.875344815512134, 14.21697484183684, 14.23511142666353, 14.830844952562549],
},
"2021": {
"x": list(range(1, 88)),
"y": [16.40998076014569, 16.131563389687667, 15.926483480935136, 15.698720075567504, 15.493215409831992, 15.18004517433272, 14.889295559356292, 14.61434191320944, 14.373082577547928, 14.186230894539502, 14.090058332837668, 14.023657754832897, 14.007435410828263, 13.98510741208859, 13.970141824822582, 13.969314479688267, 13.946015227855732, 13.928771902280433, 13.918094740825536, 13.904271041635251, 13.898094980435907, 13.884683163944276, 13.877957828236978, 13.901423741421022, 13.906131144086045, 13.902626593900637, 13.894640462593705, 13.918502460807465, 13.918292173412722, 13.936267540996683, 13.893769797887094, 13.89937529564544, 13.863770002450618, 13.867997609697417, 13.848933342732614, 13.815022252718682, 13.791564536076104, 13.785299092567266, 13.757852960538305, 13.756401654592215, 13.759718968653816, 13.733437497507486, 13.700263326582155, 13.741373305941403, 13.675414218993899, 13.706510616734628, 13.679167854117077, 13.691416726739856, 13.672658642514545, 13.70674777789251, 13.677889859468195, 13.696385833947582, 13.676118387776745, 13.712359839755743, 13.678461938862087, 13.655240869170136, 13.686341276950634, 13.718978062873198, 13.727316226517866, 13.656066612805246, 13.613789363715679, 13.743623408755314, 13.736977834220136, 13.602716719881093, 13.663563667635234, 13.62881219522605, 13.642571996285401, 13.684891127715069, 13.552729633336378, 13.722320681269425, 13.688784634409584, 13.681265629893907, 13.655688148500278, 13.889373816531247, 13.612825342345905, 13.606765349789637, 13.795350032043327, 13.82650292060858, 13.841286651582926, 13.657986925155127, 14.058341453040232, 13.751822923564026, 13.936886101042488, 13.90058603143137, 14.193189631919063, 14.225976069809075, 14.830844952562549],
},
"2022": {
"x": list(range(1, 88)),
"y": [15.816438075969113, 16.120385966332037, 15.882429676047822, 15.754141510831133, 15.3879948181939, 14.847440397508679, 14.559122186153141, 14.392608974240208, 14.271479310233566, 14.151814366776497, 14.033446724531911, 13.971139835079242, 13.946657171368635, 13.923168976370057, 13.908213326289514, 13.903962921781009, 13.883435189687164, 13.868214256361957, 13.853316203275403, 13.850156274630233, 13.83800413007661, 13.828456929678238, 13.823667080256051, 13.837379627152984, 13.85468762648662, 13.842118819095077, 13.846068401970452, 13.866145193860882, 13.87683103331106, 13.883902318309158, 13.843314542816382, 13.848955212935406, 13.825679672773983, 13.825203585467685, 13.800650657685432, 13.771987156235094, 13.745277332204294, 13.74899618896188, 13.710350289788519, 13.699009636752562, 13.72574984380712, 13.69379612714152, 13.657631333739795, 13.687110795197487, 13.641654848107489, 13.68555427745178, 13.626523037803157, 13.656569173850187, 13.64357081552712, 13.655564923630237, 13.650126005189923, 13.66108116025011, 13.636544222792033, 13.663746867549065, 13.626176670833402, 13.59824157968966, 13.674717201935882, 13.694557928731372, 13.727707429817114, 13.657670720998764, 13.54889394004633, 13.69650375543492, 13.692110648403906, 13.55263976838036, 13.630076360505104, 13.593884835135345, 13.596113596592723, 13.602738980430088, 13.490250965065599, 13.698940481247519, 13.583385809300575, 13.60991931296976, 13.581332737838016, 13.824623548139833, 13.533893973585819, 13.533775394793583, 13.755699578934374, 13.759936297399996, 13.87244513763899, 13.616014488643092, 14.09812993251356, 13.710074602518604, 13.91784066420223, 13.912311265620039, 14.203969619716446, 14.255022778711806, 14.830844952562549],
},
"2023": {
"x": list(range(1, 88)),
"y": [14.789164834058647, 14.780127256037884, 14.819678850556608, 14.733221736292204, 14.715123895744814, 14.569614305023329, 14.396346309952783, 14.267962672112828, 14.173985658599138, 14.070884007245896, 13.974284099164725, 13.918302031044524, 13.898843341205037, 13.879919458330534, 13.865098670719211, 13.851559827592494, 13.844906626448243, 13.827295800961403, 13.820810936762644, 13.809543815857673, 13.804536613446562, 13.804859924468062, 13.785889612091664, 13.802986923401033, 13.830453100698728, 13.816370948078971, 13.830157632336801, 13.839051100924063, 13.84746613758983, 13.860297269342437, 13.815378459306658, 13.812782079759149, 13.793416550465908, 13.815821643865219, 13.78691014152164, 13.740292216550131, 13.709841553660574, 13.745287154982947, 13.691633688757076, 13.67702798250515, 13.665734867137164, 13.660225685620617, 13.636580976940015, 13.658398179452709, 13.607051808944078, 13.641436611855841, 13.593798625450953, 13.61361178624699, 13.638190686281677, 13.646499055714083, 13.591038080776018, 13.622992917555028, 13.587839722276074, 13.630829824495837, 13.629461225299615, 13.592200956351647, 13.615680520490306, 13.630341745716859, 13.677426157763774, 13.626805264232148, 13.564525550141198, 13.67038341024563, 13.672981832082215, 13.525896811417093, 13.563993050553714, 13.611941675078645, 13.553760533281189, 13.59021111451196, 13.470082500603052, 13.59912543494902, 13.509683974597134, 13.521390742472398, 13.520602741034393, 13.725732516220367, 13.402650383856045, 13.482679094860476, 13.645693009561771, 13.775875527667997, 13.832068021284835, 13.498004659433413, 14.094763215759677, 13.660926796721734, 13.834609766490832, 13.863488794330497, 14.180579199558213, 14.222775721949759, 14.830844952562549],
},
}
fig = go.Figure()
# Add a line for each year
for year, values in data.items():
x_values = values["x"]
y_values = values["y"]
fig.add_trace(go.Scatter(
x=x_values,
y=y_values,
mode='lines+markers',
name=year
))
# Update layout
fig.update_layout(
title="Perplexity Across Different Dump Duplication Counts",
xaxis_title="Number of Dumps Duplication",
yaxis_title="Average Perplexity",
legend_title="Year"
)
# Show figure
graph6 = fig
#llama graph 1
import plotly.graph_objects as go
# Data for different years
data = {
"2014": [10.036774097041135, 9.306693996275795, 9.442327622499175, 9.015408185880002, 9.94583162297666, 9.141712571508352],
"2015": [9.46310273785878, 8.763464863196129, 9.075851726027564, 8.868392446242012, 9.113560631617027, 8.037411460181893],
"2016": [9.41413706166537, 8.645126825996691, 9.527148465147846, 9.120345162203675, 8.9228845723255, 9.14052983061081],
"2017": [9.50318602661455, 9.473904977192573, 9.755998086072951, 8.968012141869462, 8.895860780054043, 8.757970647106037],
"2018": [9.007669062339426, 10.95829859145081, 10.128151243953157, 9.451949410987668, 8.863879736723902, 8.440366034517687],
"2019": [8.388255660116407, 10.676105294328789, 9.728353939624842, 9.381837094065533, 8.401723232809463, 7.5705604983353325],
"2020": [10.112246017864624, 10.255251179892559, 9.233548505479437, 9.25131862646364, 8.458532176757009, 7.4808205167223525],
"2021": [10.239269162661959, 9.54987953569235, 9.067380903629866, 9.014261939731549, 8.14345667720481, 7.312019290288715],
"2022": [9.931951075969451, 9.12737570591033, 8.995868137602248, 9.00805668763514, 7.882044010499616, 7.538858258386088],
"2023": [8.646614152066428, 8.806922449908505, 8.816629232137835, 8.995152677487027, 7.737747701620713, 6.77703951001925]
}
# Buckets
buckets = ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"]
# Create the figure
fig = go.Figure()
# Add lines for each year
for year, perplexities in data.items():
fig.add_trace(go.Scatter(x=buckets, y=perplexities, mode='lines+markers', name=year))
# Update layout
fig.update_layout(
title="Perplexity Across Different Buckets",
xaxis_title="Bucket (duplicate count range)",
yaxis_title="Average Perplexity",
legend_title="Year"
)
# Show the figure
llama_graph1 = fig
##llama graph 2
import plotly.graph_objects as go
# Data for different years and buckets
data = {
"1-1": [10.036774097041135, 9.46310273785878, 9.41413706166537, 9.50318602661455, 9.007669062339426, 8.388255660116407, 10.112246017864624, 10.239269162661959, 9.931951075969451, 8.646614152066428],
"2-5": [9.306693996275795, 8.763464863196129, 8.645126825996691, 9.473904977192573, 10.95829859145081, 10.676105294328789, 10.255251179892559, 9.54987953569235, 9.12737570591033, 8.806922449908505],
"6-10": [9.442327622499175, 9.075851726027564, 9.527148465147846, 9.755998086072951, 10.128151243953157, 9.728353939624842, 9.233548505479437, 9.067380903629866, 8.995868137602248, 8.816629232137835],
"11-100": [9.015408185880002, 8.868392446242012, 9.120345162203675, 8.968012141869462, 9.451949410987668, 9.381837094065533, 9.25131862646364, 9.014261939731549, 9.00805668763514, 8.995152677487027],
"101-1000": [9.94583162297666, 9.113560631617027, 8.9228845723255, 8.895860780054043, 8.863879736723902, 8.401723232809463, 8.458532176757009, 8.14345667720481, 7.882044010499616, 7.737747701620713],
"1001-30000000": [9.141712571508352, 8.037411460181893, 9.14052983061081, 8.757970647106037, 8.440366034517687, 7.5705604983353325, 7.4808205167223525, 7.312019290288715, 7.538858258386088, 6.77703951001925]
}
# Years
years = ["2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"]
# Create the figure
fig = go.Figure()
# Add lines for each bucket
for bucket, perplexities in data.items():
fig.add_trace(go.Scatter(x=years, y=perplexities, mode='lines+markers', name=bucket))
# Update layout
fig.update_layout(
title="Perplexity Across Different Years",
xaxis_title="Year",
yaxis_title="Average Perplexity",
legend_title="Bucket (duplicate count range)"
)
# Show the figure
llama_graph2 = fig
#llama graph 4
import plotly.graph_objects as go
# Data for different years and dump duplications
data = {
"2014": {
"dumps": [1, 2, 3, 4, 5, 6, 7],
"perplexities": [9.73915022954687, 8.917501442736835, 10.870824235415164, 8.839485979263355, 9.114611185408386, 9.00595271327483, 9.069847072965578]
},
"2015": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
"perplexities": [9.219127989173247, 8.610711104284512, 8.505043568675715, 7.990039784261444, 8.107957918641446, 8.796945238229792, 8.013223752027935, 8.890635712836795, 8.83059261426682, 10.04094169141142, 8.570752850736193, 9.003542320883396, 8.587190578806105, 7.983045509484727]
},
"2016": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
"perplexities": [9.224890447512712, 8.982140506828461, 7.624091188629551, 8.97063321078077, 8.261724045845817, 7.793111396647548, 7.829316622145423, 8.597365842164928, 9.301981760525631, 9.66361415155858, 9.258511719598742, 9.441774097297651, 9.429412341325685, 9.325699904738741, 8.836147021549138, 8.607104273128627, 8.722839588316196, 8.920039351586981, 8.914454312645233, 8.899123060759182, 9.793833500279591, 10.134616145581552, 8.353063455545444]
},
"2017": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],
"perplexities": [9.488743166894329, 9.683975569858143, 8.654505727247006, 8.066421362212841, 8.395709115228499, 8.500311197527102, 9.10900254771213, 7.794618707693523, 8.059130491102199, 7.840987528913394, 8.94443662009675, 8.811736884161157, 9.395950762255111, 8.88696403862443, 9.064476456994532, 9.474527237081157, 8.91858984698398, 9.008242064314263, 8.840440114974651, 7.978242830053041, 8.653702083122754, 9.395390940155544, 8.644847872201382, 9.017641716864084, 8.731668787167907, 7.294766100138992, 9.116288498873331, 8.812157001137116, 8.229244256822179, 10.705494840702238, 8.4287361645181, 9.127134117671186, 8.418280990577143, 8.223315656547781]
},
"2018": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46],
"perplexities": [9.896529727642656, 9.375668627700882, 9.53229676611742, 9.709666311696747, 9.371857870215996, 9.756320954898577, 9.3489352873219, 9.41874088848098, 9.220103832539115, 9.493717136086154, 9.894783254332049, 10.271325431375882, 9.444295310961042, 9.259722713114918, 9.121852071096205, 10.266770364074752, 9.322189177071575, 9.200133535506806, 9.391739906453976, 9.166754675924274, 9.386922783373333, 9.269174320074539, 8.921691129048623, 8.500872698966008, 8.427801104408742, 8.957096089538366, 8.843138224464884, 8.839757712227849, 8.730159045861498, 8.761283943113895, 8.955585790830773, 9.480108588809301, 8.975279317370992, 8.647350666497408, 8.040519395112794, 8.154306078995903, 9.047836211768528, 8.388013770335506, 9.27023552172876, 8.392778372966038, 9.698769530780428, 8.48774562040954, 9.832837942793894, 8.801856106666897, 9.090280166741453, 8.066384841477534]
},
"2019": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58],
"perplexities": [9.32068228893817, 11.36279410700072, 9.729296413744873, 10.951540717951199, 9.571599362297537, 8.746033912439227, 8.911359607667526, 8.652716464937903, 8.90908579802875, 9.526405246155608, 8.84494033246573, 9.45556467668226, 9.61666194147301, 9.471820461436957, 9.385272422926384, 9.300484355205835, 9.374562863672638, 8.921445075261644, 8.457279643164815, 8.911117277418406, 8.076917631172464, 8.58392948648068, 9.138574660565174, 9.03583800697058, 8.706482138788626, 8.581302039813796, 8.567006611519137, 5.454819541045107, 8.380493341695487, 8.129823277176698, 7.346580306828866, 8.182982036096053, 8.03219664626822, 8.434163783307083, 8.148075720769123, 8.20825575224898, 7.7034049100948945, 8.319494286475098, 8.449794025203127, 8.40448676995273, 8.394065958286836, 8.431905407945235, 8.684778696918817, 10.170628597487527, 7.685742913353763, 8.317540224765809, 7.100457493121143, 5.389889122637774, 6.52302432138372, 8.895820543553361, 8.540964121500908, 5.823656104193133, 8.946389481295038, 8.03972593805594, 7.406476375508138, 7.434287132433397, 8.592981328562821, 9.803727252301018],
},
"2020": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67],
"perplexities": [10.012609650486192, 7.943056443498068, 8.44096110442561, 9.029669769296477, 8.409724985539306, 8.731282990251563, 8.434833756562051, 8.497304934371442, 8.467244156051745, 8.606236488210545, 8.805728093624284, 9.375705002747582, 9.21767027458378, 9.048307919892986, 9.77562185114431, 8.730686261181575, 9.434356327584526, 9.119611839878791, 9.17722037907838, 8.569631235903184, 8.899794145233415, 9.315415673275877, 8.170599811786479, 8.538223762760031, 8.19667246899941, 8.740262061758479, 8.335448351341697, 8.321473204597588, 8.505543388481339, 8.552966240767482, 8.350236112276809, 8.109081033072668, 8.539929546398097, 8.535623890267809, 8.152345891530405, 8.399872141360548, 7.737922667873269, 8.15695720088359, 8.175043636189214, 7.595645045643389, 7.911222051300795, 8.645606075472527, 8.114112476153545, 8.054768997187903, 7.800103557084926, 8.096027154350947, 8.10653262234877, 8.029447458295808, 7.192697926255588, 9.370330492895473, 8.523467886567657, 7.313215625083473, 8.839958839301076, 8.656716147817143, 8.137974531785895, 8.523363904379156, 6.108894835851177, 7.818953624010673, 8.49431208395812, 8.342596121020756, 7.742470144320784, 6.682052985671971, 6.759804959168626, 9.093551460193316, 7.062030527371495, 8.434506543833649, 6.781692983120098],
},
"2021": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76],
"perplexities": [9.841089195168713, 8.366233239891208, 8.059521351436208, 8.318918468325863, 8.324706089968892, 8.32605366049384, 8.435930736985704, 8.66800508008913, 8.406990633173743, 8.962431358959584, 8.850717354466074, 9.1716199955458, 9.302688993535714, 8.939046098190769, 9.051679849424985, 8.682789687366284, 8.994991585405426, 8.561380912367396, 8.787500555168679, 8.843251281455629, 8.7217706466806, 8.381660448144187, 9.395216774912862, 8.591359926732526, 8.453429031525232, 8.456509570151965, 8.628116911794814, 8.53819222436614, 8.730438299562152, 9.376235492300244, 8.15154442462199, 8.54875158466325, 8.163866477674533, 8.304409143008455, 8.856929571720633, 8.167913697870857, 7.84492260274128, 8.162407286516313, 8.321838697740313, 8.253127215455834, 8.248563253933684, 8.499374467029739, 8.680742460396948, 7.967559876484448, 8.095069183089077, 7.912424470771882, 7.7624804836167565, 7.807769541801359, 8.016590141394108, 7.6555859978103316, 8.416070459989983, 7.512191560306614, 7.994576039964321, 7.185188422240499, 7.660311594811295, 7.23106290817905, 6.982644852723388, 7.843742555580493, 7.005057894403877, 8.430250121211994, 5.657881355879867, 6.726292542438366, 7.486937111190708, 7.712303495834796, 6.246962399681747, 6.92875404699629, 5.329766234792039, 5.709327997807491, 7.971206942045293, 5.686373279239376, 6.1781744792525615, 8.241084719551052, 8.439160967397711, 7.65310078654845, 6.327069191350794, 3.91910919080523],
},
"2022": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82],
"perplexities": [9.483592568856565, 7.189994228954454, 8.316909239194644, 7.86424678656687, 9.220674611582542, 8.321976529350732, 9.10888002884781, 8.225969911900018, 8.384237608546773, 8.780327271679809, 9.364145618644377, 9.495859713757827, 9.106949174218812, 9.23421176581141, 9.163921762844911, 8.794794461344875, 8.828930317074237, 8.910619607048394, 8.875861267417038, 8.806690212582172, 9.061056708418198, 8.866883414667015, 8.67286618308663, 8.682273027254936, 8.305102405030174, 8.189733511502775, 8.924337748216326, 8.659728319566609, 8.444422835653581, 8.755250021389994, 8.000000289145156, 8.081525926632303, 8.061721834769063, 7.895475427973516, 8.287032632326136, 8.472773038016852, 7.663326197885218, 7.847535650315383, 7.732755997953167, 7.960257016089297, 7.608447426559309, 7.598389147784626, 7.946761405541756, 7.773873549407202, 8.126908611820289, 7.916664649722816, 7.894622086606636, 7.680503580812927, 7.727898320658088, 7.432772901777352, 8.202398282489195, 8.042184753937285, 7.73548358717129, 7.682704039184239, 8.158164009607694, 7.345186708566949, 7.828163052277644, 7.611824744941703, 7.241808787698759, 7.582892852952833, 7.879397809151155, 7.134581497547041, 7.78667356422719, 6.656397614763345, 7.023352560594163, 7.362930730205023, 7.3699197483652386, 6.7454140379422265, 5.903262622771099, 6.434879224359672, 7.306608742238141, 7.9100884219615555, 7.924947631037447, 8.120669403708789, 7.8919156097528464, 7.295660656533646, 5.599201327007343, 5.260468504600074, 6.889443342018529, 5.396932289937023, 6.7357812331788285, 7.079240389198726],
},
"2023": {
"dumps": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87],
"perplexities": [8.70489099557147, 7.743567081947979, 5.707700759513075, 7.42267071929539, 7.528257194741652, 6.766266255314377, 8.140487690026744, 7.968460364579144, 8.378534732391445, 8.462864835761868, 8.750163467122363, 9.030159982511133, 8.947498611252533, 9.26185161266797, 9.148155404351185, 9.160868347246595, 9.132536983584458, 9.12916497406581, 9.027825944368539, 9.127169658958355, 9.087762413460762, 9.288851446582335, 9.21606664952798, 9.10292838042461, 9.142519978568224, 8.822355646917647, 8.900446619619732, 8.79981304565808, 8.918708459617246, 9.13267243596762, 8.759214105765599, 8.961577284078654, 8.12181490449537, 8.43888231714366, 8.724831350613316, 8.258762532936931, 8.337583232620341, 8.223678511207945, 8.19914152935067, 8.250449659843008, 8.488274186714182, 8.063794656047826, 8.267469501822353, 8.109403400519883, 7.897519766033864, 8.029859903259943, 8.01415790723292, 7.852726835204203, 7.524006414246027, 8.016485804552786, 8.181980185953115, 7.7865858384362445, 7.630488133713028, 7.744179998387641, 7.9618609986528766, 7.818104340203628, 7.873732400580378, 7.643522973769515, 7.743819932052891, 7.603304862075835, 7.499516191588959, 7.919857492185231, 7.294200517555408, 7.835098137549298, 7.562994060655124, 7.426074658906729, 7.4503303575596185, 7.6402144041645315, 6.889172499203354, 7.869082402525025, 7.476479798274207, 7.46012411648056, 7.016878527752248, 7.233305845472016, 6.657949958745544, 6.2352925881040875, 6.558931554888652, 6.094819134587699, 6.444696396841287, 5.338755088514068, 5.367611973164904, 5.425863412036782, 4.20042362030674, 5.111941016244413, 5.149295160813693, 3.9082208202377435, 2.6866750639387136],
},
}
# Create figure
fig = go.Figure()
# Add traces for each year
for year, year_data in data.items():
fig.add_trace(go.Scatter(x=year_data["dumps"], y=year_data["perplexities"], mode='lines+markers', name=year))
# Update layout
fig.update_layout(
title="Perplexity Across Different Dump Duplication Counts",
xaxis_title="Number of Dumps Duplication",
yaxis_title="Average Perplexity",
legend_title="Year"
)
# Show the figure
llama_graph4 = fig
#llama graph 5
# Data for different buckets and years
import plotly.graph_objects as go
# Data for different years and buckets
data = {
"2014": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.276942939297756, 8.69938211424056, 8.158271167692497, 8.367670702299348, 6.583755343348351, 7.497674909399879]
},
"2015": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.103981560121444, 8.198551019737206, 8.17752561770772, 8.233928498586414, 6.517481691960036, 7.3736492145399355]
},
"2016": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.023271012270133, 8.067050538323626, 8.150103850870096, 8.232721560484496, 6.3749350052179015, 7.350697996200808]
},
"2017": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.129487810210167, 8.290815319993621, 7.878257519772607, 7.98292071692709, 6.524893452272168, 6.918924770013169]
},
"2018": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.213411471804902, 7.89026459259236, 7.403516159589743, 7.556076611087452, 6.6509342133282745, 6.493818412135467]
},
"2019": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [8.96407327327958, 7.628975687395983, 7.036189728869401, 6.6349379440939895, 6.446837262269017, 6.086615106309735]
},
"2020": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.137673180826452, 7.794611238323424, 7.059198583303955, 6.82070782491814, 6.64418110544852, 6.271177318179787]
},
"2021": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.178752781806578, 7.70543845537127, 6.698443323550492, 6.472409310902069, 7.307749809853384, 6.782598533191676]
},
"2022": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [9.096031774523855, 7.652072280274248, 6.615135590934534, 6.239500427643811, 6.787412545253696, 6.99638904682498]
},
"2023": {
"buckets": ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"],
"perplexities": [8.728460642325789, 7.561677894180029, 6.194391269071906, 5.775947387872692, 6.054439209079063, 5.3384410032299]
}
}
# Create figure
fig = go.Figure()
# Add traces for each year
for year, year_data in data.items():
fig.add_trace(go.Scatter(x=year_data["buckets"], y=year_data["perplexities"], mode='lines+markers', name=year))
# Update layout
fig.update_layout(
title="Perplexity Across Different Buckets",
xaxis_title="Bucket (Duplicate Count Range)",
yaxis_title="Average Perplexity",
legend_title="Year"
)
# Show the figure
llama_graph5 = fig
##llama graph 6
# Data for different years and duplication counts
data = {
"2014": {
"duplicates": list(range(1, 88)),
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"2023": {
"duplicates": list(range(1, 88)),
"perplexities": [8.70489099557147, 7.580704259900713, 5.836272117595713, 7.267518477506005, 7.283085974734155, 5.803062744865375, 8.134525344019872, 7.557124524037357, 8.285446726695811, 8.351123312334009, 8.590647921692078, 9.005573576795152, 8.871116907846343, 9.192666716497001, 9.005592946042935, 9.143088639505718, 9.091899749285021, 8.968510745275767, 8.975056578670769, 9.091779598794488, 8.980027479169559, 9.159988763765217, 8.883393229479356, 8.874910963718058, 8.931427901889217, 8.625523963019026, 8.735000607943736, 8.63742312596692, 8.70111762921543, 9.206595504882097, 8.763642440521853, 8.911783192379705, 7.634828216241755, 8.259433628637158, 8.82001651315211, 8.107726825798297, 8.206921799311043, 8.355228813696131, 8.298238657805719, 8.033613885206083, 8.225747435920205, 7.934196000879481, 8.286077333650876, 7.980486854114626, 7.721311081423362, 7.689565789535576, 7.790611228082437, 7.624460645122273, 7.0093123271820055, 7.791169711054489, 8.14175443228974, 7.776459425768002, 7.526576191153727, 7.756713091828537, 7.801566191856336, 7.843167604084829, 7.7089466527367705, 7.409181212687685, 7.442037119594963, 7.337342474375209, 7.190330728288846, 7.254100247522111, 6.79586626781295, 7.483337535174346, 7.294262214559599, 7.030373799134058, 7.149000704995256, 7.324234704301861, 6.822226914595191, 7.792044674564248, 7.149607692275065, 7.078295043693808, 6.946740616609221, 6.700263270499871, 6.4699642011179925, 6.359739677958311, 6.494171402228005, 5.966063278273302, 6.15136761591695, 5.309723356734837, 5.00827796373236, 4.976659481606136, 4.042120043465434, 4.842002060938444, 5.114027203055052, 3.8900363946754233, 2.6866750639387074],
},
}
# Create figure
fig = go.Figure()
# Add traces for each year
for year, year_data in data.items():
fig.add_trace(go.Scatter(x=year_data["duplicates"], y=year_data["perplexities"], mode='lines+markers', name=year))
# Update layout
fig.update_layout(
title="Perplexity Across Different Dump Duplication Counts",
xaxis_title="Number of Dumps Duplication",
yaxis_title="Average Perplexity",
legend_title="Year"
)
# Show the figure
llama_graph6 = fig
##txt360 vs fineweb comparison table
dataset_comparison = pd.DataFrame(
{
"Metric": [
"BoolQ",
"PIQA",
"HellaSwag",
"Winogrande",
"MMLU",
"Natural Questions",
"TriviaQA",
"GSM8K",
"MATH",
"MedQA",
],
"TxT360 - Upsampling": [
"70.31",
"80.36",
"73.54",
"68.43",
"30.26",
"22.22",
"58.52",
"3.41",
"28.04",
"25.61",
],
"FineWeb": [
"71.5",
"82.1",
"79.46",
"70.96",
"28.5",
"19.03",
"36.65",
"5.31",
"29.65",
"27.26",
],
"TxT360 Difference": [
"(1.19)",
"(1.74)",
"(5.92)",
"(2.53)",
"1.76",
"3.19",
"21.87",
"(1.9)",
"(1.61)",
"(1.65)",
],
}
)
styled_table = (
dataset_comparison.style.applymap(
lambda _: "background-color: #E1EEDB", # Green background for "TxT360" and "AnotherColumn"
subset=pd.IndexSlice[:, ["TxT360 - Upsampling", "TxT360 Difference"]] # Apply to both columns
)
.applymap(
lambda _: "background-color: white", # White background for all other columns
subset=pd.IndexSlice[:, dataset_comparison.columns.difference(["TxT360 - Upsampling", "TxT360 Difference"])] # Apply to all columns except "TxT360" and "AnotherColumn"
)
.hide(axis="index") # Hide the row index
)
table_html = styled_table._repr_html_()
table_div_1 = Div(NotStr(table_html),
style="display: flex; justify-content: center; align-items: center; width: 100%; max-width: 100%; height: auto; overflow-x: auto;"
)
intro_div = Div(
H2("TxT360 Studies"),
H3("What This Section Contains"),
P("This section shows the learning curve when pre-training on TxT360, with a proper upsampling approach. We compare several simple strategies and demonstrate that one particular upsampling method, inspired by the natural data distribution, performs exceptionally well. In our preliminary experiments, the model learns significantly faster on TxT360 compared to a similarly scaled dataset, FineWeb, on several important evaluation metrics. We believe that a more carefully designed upsampling strategy could further enhance the use of our data."),
P("In addition to the training results, we also provide an analysis of the dataset, including perplexity trends over time across the CommonCrawl snapshots. This section is organized into the following topic areas:"),
Ul(
Li("The Learning Curve of TxT360 with an Upsampling Recipe", style = "margin-bottom: 5px"),
Li("Perplexity Analysis across time", style = "margin-bottom: 5px"),
Li("Topic Analysis on Data Cluster Groups", style = "margin-bottom: 5px"),
Li(B("Estimated Reading Time: 25 minutes"), style = "margin-bottom: 5px"),
),
)
upsampling_exp = Div(
H2("A Simple Data Mix Creates a Good Learning Curve"),
P(
"As discussed in prior sections, duplicated documents can significantly reduce training efficiency (i.e., the ratio of model performance to the number of pre-trained tokens). Previous work, such as RefinedWeb",
D_cite(bibtex_key="refinedweb"),
", emphasizes the importance of deduplication. Recently, the FineWeb study conducted an interesting analysis, comparing LLM performance when pre-trained on globally deduplicated versus locally deduplicated datasets. They found that training efficiency with a globally deduplicated dataset can be worse",
D_cite(bibtex_key="fineweb"),
". Fineweb hypothesize that global deduplication may remove a higher proportion of high-quality documents."
),
P(
"This finding led us to consider that a pre-training corpus based on crawled websites is naturally upsampled for a variety of reasons. For example, commonly used templates or boilerplates may appear millions of times; a well-regarded article reposted by different users may surface across multiple sites; and the same web pages, crawled by CommonCrawl at different times, will duplicate each other. The reasons behind these duplications vary: some may serve as indirect indicators of high-quality content, while others may not. Therefore, curating a pre-training dataset should involve leveraging these signals and considering data weighting schemes — or at the very least, provide users with the necessary information to control it effectively."
),
P(
"To this end, we store rich metadata for each document source, including features like user votes from StackExchange. One crucial piece of metadata is the number of duplicates detected for a document. This information allows users to reconstruct the natural web distribution, but more importantly, we will demonstrate that a simple upsampling recipe based on this metadata can create a high-quality data mix."
),
H3("Experiment Setup"),
P(
"Motivated by the FineWeb study, we opted to upsample documents based on their natural distribution. However, since duplication is only an indirect indicator of quality, we upsample documents to a few predefined levels rather than using their exact count. Specifically, we set the upsampling weight to 3 for documents with 2 to 5 duplicates, 5 for those with 5 to 100 duplicates, 8 for 101 to 1000 duplicates, and 10 for documents with over 1000 duplicates. These values were selected heuristically and informed by preliminary small-scale experiments. For non-CommonCrawl data sources, we assign a weight of 2 if the document appears more than once. This straightforward approach results in a corpus exceeding 15 trillion tokens, making it one of the largest open-access pre-training datasets available."
),
P(
"To evaluate the training efficiency of our dataset, we sampled 1.5T tokens from both FineWeb and TxT360 (using the aforementioned weighting) and conducted a training ablation on an 8x8B Mixture-of-Experts architecture, similar to Mixtral. We compared the learning curves by tracking training loss, validation scores, and performance across a wide array of diverse evaluation benchmarks. The validation set was sampled independently from SlimPajama",
D_cite(bibtex_key="cerebras2023slimpajama"),
". Note that this experiment is done on a slightly earlier version of the dataset."
),
H3("Learning Curves on the Evaluation Metrics"),
P(
"Evaluation results are the most direct indicator of model quality. We assess the intermediate results of the models across multiple metrics and plot the learning curves. Our findings indicate that the model learns significantly faster with TxT360. For a fair comparison, we evaluate TxT360 against FineWeb using only the CommonCrawl data sources, and we also show the curves after incorporating the 14 curated sources and coding data (Stack V2), demonstrating the full potential of the dataset. Due to computation resource constraints, we stop running experiments when we can observe clear trends."
),
P(
"Based on the metrics, we find that TxT360’s CommonCrawl portion with the umsampling strategy outperforms FineWeb on key metrics at MMLU, NQ, falls slightly behind on HellaSwag. Furhter, we show that by combining TxT360 with coding data (Stack V2), the learning curve is significantly more stable and we observe improved results across most all of the metrics. Apparently the dataset preference here may depend on the set of metrics one would use."
),
plotly2fasthtml(all_eval_res_figs["MMLU"]),
plotly2fasthtml(all_eval_res_figs["NQ"]),
plotly2fasthtml(all_eval_res_figs["HellaSwag"]),
P(
"Similar to the findings in DCLM, adding the curated non-CommonCrawl data sources produces mixed results (some preliminary figures are not shown here). Yet such data can help with domain specific tasks like MedQA."
),
plotly2fasthtml(all_eval_res_figs["MedQA"]),
plotly2fasthtml(all_eval_res_figs["PIQA"]),
plotly2fasthtml(all_eval_res_figs["TriviaQA"]),
plotly2fasthtml(all_eval_res_figs["WinoGrande"]),
H3("Comparing the Loss Curves"),
P(
"We also plot the training and validation loss curves for each dataset, showing that TxT360 achieves both lower training and validation losses compared to FineWeb. Although training loss may not correlate directly with final model performance, we observe that the loss curve for TxT360 exhibits fewer spikes compared to FineWeb, indicating more stable training dynamics."
),
plotly2fasthtml(lm_loss_graph),
plotly2fasthtml(validation_loss_graph),
)
preplexity_intro_div = Div(
H2("Perplexity Evaluation on Duplicate Data"),
H3("Model based Quality Estimation"),
P("We took one of the model-based data quality evaluation strategies adopted by ", A("DataComp-LM",href="https://arxiv.org/abs/2406.11794"), " which used perplexity filtering as a candidate for quality filtering. The DCLM results show that a simple perplexity filter is still quite strong. DCLM followed ", A("CCNet’s",href="https://arxiv.org/abs/1911.00359"), " practice to use a 5-gram Kneser-Ney model as implemented in the ",A("KenLM",href="https://github.com/kpu/kenlm"), " library for efficient perplexity calculation. In order to gain more insights of our dataset, we also took a ", A("KenLM model",href="https://huggingface.co/edugp/kenlm"), " trained on English Wikipedia data to compute perplexity on data with different duplication patterns, and try to observe how such signals coorelate with the duplication patterns."),
H3("Sampling Strategy"),
P("We took a early version of the TxT360 Common Crawl (CC) portion, and bucket the documents by the number of duplicates each has. For each CC snapshot, we bucket the documents by their duplicate counts in the following buckets (1, 2-5, 6-10, 11-100, 101-1000, 1001-infinite). We sampled the first 10k documents from each bucket."),
)
perp1_div = Div(
# this looks basically the same as the figure below, comment it out for now.
# Section(
# H3("Perplexity vs Buckets"),
# P("For each bucket, we aggregated all the chunks that belong to a single year and calculated the average perplexity for each (bucket, year) data point. We observe the perplexity is generally dropping. This could be biased since we always keep the newest document if we find a duplicate."),
# #Img(src="images/prep-diff-buckets-global.png", height = "300", width = "600" ),
# plotly2fasthtml(Perplexity_Across_Different_Buckets_global_graph),
# ),
Section(
H3("Perplexity vs. Years"),
P("Taking the same data, we can convert it into a graph indicating the yearly trend. For most buckets, the average perplexity of dumps from more recent years seem to be lower than that of former years. This could be biased since we always keep the newest document if we find a duplicate."),
#Img(src="images/prep-across-diff-year-global-dup-buckets.png", height = "300", width = "600" ),
plotly2fasthtml(graph2222),
),
Section(
H3("Perplexity vs. Document Duplication"),
P("Instead of bucketing, we also plot the relationship between perplexity versus the number of duplicates directly. The graph becomes a bit noisy at the end because of insufficient samples with larger duplication counts. However, we can observe that there seems to be a lower point at around 10-20 duplicates. To see the results more clearly, we recommend you turn of other years and only look at one year, and zoom in to 0-100 region on the X axis."),
#Img(src="images/prep-across-diff-docs-dup-count-global.png", height = "300", width = "600" ),
plotly2fasthtml(graph3),
),
Section(
H3("Perplexity vs. Dump Duplication"),
P("Fineweb hypothesize that documents appear across multiple snapshots (CC dumps) might be an indicator of quality. Hence, we also plot the perplexity versus the number of times a document appear in different snapshots. From the graph below we can see that documents that are duplicated across around 40 - 60 snapshots usually have lower perplexity."),
#Img(src="images/prep-across-diff-dump-dup-counts-global.png", height = "300", width = "600" ),
plotly2fasthtml(graph4),
),
Section(
H3("Perplexity Plots before Global Deduplication"),
P("Previously we have seen that documents in recent snapshots tend to have lower perplexity. This might be related to the way how global deduplication was implemented. During global deduplication, we only keep copy in the latest dump. Hence documents that are duplicated across multiple dumps only appear in the latest one. To avoid bias brought by this strategy, we tried to recover the states before the global deduplication using the stored metadata (i.e., the locally deduplicted dataset state). This trends are a bit different. In the figure below, we do not observe a clear trend of which year has a higher quality, especially in the 2-10 bucket region."),
#Img(src="images/prep-across-diff-buckets-local.png", height = "300", width = "600" ),
plotly2fasthtml(graph5),
),
Section(
H3("Perplexity vs. Dump Duplication before Global Deduplication"),
P("Following the same practice, we can plot the graph of average perplexity with respect to dump duplication count, before global deduplication. The conclusion is similar, that documents with a dump duplication count around 40-60 have the lower perplexity."),
#Img(src="images/prep-diff-dump-dump-counts-local.png", height = "300", width = "600" ),
plotly2fasthtml(graph6),
),
)
llama_div = Div(
Section(
H2("Llama 3.1 8B"),
P("For comparison purpose, we run the same perplexity evaluation with llama 3.1 8B model."),
),
Section(
H3("Perplexity vs. Buckets"),
#Img(src="images/perp-across-diff-buckets-global.png", height = "300", width = "600" ),
plotly2fasthtml(llama_graph1),
),
Section(
H3("Perplexity vs. Years"),
#Img(src="images/prep-across-diff-years-global.png", height = "300", width = "600" ),
plotly2fasthtml(llama_graph2),
),
Section(
H3("Perplexity vs. Dump Duplication"),
#Img(src="images/prep-vs-dump-dup-global.png", height = "300", width = "600" ),
plotly2fasthtml(llama_graph4),
),
Section(
H3("Perplexity vs. Buckets before Global Deduplication"),
#Img(src="images/prep-diff-buckets-local.png", height = "300", width = "600" ),
plotly2fasthtml(llama_graph5),
),
Section(
H3("Perplexity vs. Dump Duplication Count before Global Deduplication"),
#Img(src="images/prep-vs-dump-dup-global.png", height = "300", width = "600" ),
plotly2fasthtml(llama_graph6),
),
)
with open(os.path.join(os.path.dirname(__file__), "data", "topic_charts.json"), 'r') as f:
topic_charts = json.load(f)
topic_graphs = []
for title, data in topic_charts:
if data["type"] == "barh":
topic_graphs.append(go.Figure(go.Bar(
x=data["kwargs"]["width"],
y=data["kwargs"]['y'],
orientation='h',
marker_color=[
"rgb(" + ", ".join(str(val * 255) for val in rgb) + ')'
for rgb in data["kwargs"]["color"]
]
)))
elif data["type"] == "pie":
topic_graphs.append(go.Figure(go.Pie(
values=data["kwargs"]['x'],
labels=data["kwargs"]["labels"],
marker_colors=[
"rgb(" + ", ".join(str(val * 255) for val in rgb) + ')'
for rgb in data["kwargs"]["colors"]
]
)))
cluster_div = Div(
Section(
H2("Topic Analysis"),
P("In order to understand our dataset better, we tried to cluster our data into topic groups and examined for correlations between topics and other attributes of the documents. We suspect documents from different topic groups should manifest different characteristics of distribution, which can give us some insight into the composition of dataset."),
H3("Methodology"),
P("We took an early version of the LLM360 Common Crawl portion and clustered them into 17 topic groups using ", A("BERTopic", href="https://maartengr.github.io/BERTopic/index.html"), ". We collected and aggregated a series of metrics from the stored metadata. For each topic group, we calculated average scores and generated the corresponding bar charts over different metrics for comparison and analysis."),
H3("Cluster Groups"),
P("We grouped data into the following 17 clusters. These clusters are obtained by first clustered a seed portion of the dataset into 128 dumps, and then we manually inspect the clusters to combine 17 semantically meaningful ones."),
Ul(*(
Li(topic_name, style = "margin-bottom: 5px")
for topic_name in ("Arts", "Business & Economics & Finance", "Culture & Cultural geography", "Daily Life & Home & Lifestyle", "Education", "Entertainment & Travel & Hobby", "Environment", "Food & Drink & Cooking", "Health & Wellness & Medicine", "Law & Justice", "Natural Science & Formal Science & Technology", "Personal Development & Human Resources & Career", "Politics & Government", "Religion & Spirituality", "Shopping & Commodity", "Society & Social Issues & Human Rights", "Sports")
)),
H3("Topic vs. Various Metrics"),
P(
"In the following section, we plot the cluster against their average score of a particular metric stored in the metadta. We recommend the readers to jump to the ones you are most interested in."
),
*(
Section(H4(title), plotly2fasthtml(topic_graphs[i]), P(data.get("comment", '')))
for i, (title, data) in enumerate(topic_charts)
)
)
)
def results():
return Div(
Section(
intro_div,
id="section51"
),
Section(
upsampling_exp,
id="section52"
),
Section(
preplexity_intro_div,
id="section53"
),
Section(
perp1_div,
id="section54"
),
Section(
llama_div,
),
Section(
cluster_div,
id="section55"
),
id="inner-text"
)
|