results.py

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


## Evaluation Graphs

# Load the data
all_eval_results = {}
for fname in os.listdir("data/txt360_eval"):
    if fname.endswith(".csv"):
        metric_name = fname.replace("CKPT Eval - ", "").replace(".csv", "")
        all_eval_results[metric_name] = {}

        # with open(os.path.join("data/txt360_eval", fname)) as f:
        df = pd.read_csv(os.path.join("data/txt360_eval", fname))

        # slimpajama_res = df.iloc[2:, 2].astype(float).fillna(0.0) # slimpajama
        fineweb_res = df.iloc[2:, 4].astype(float).fillna(method="bfill") # fineweb
        txt360_base = df.iloc[2:, 5].astype(float).fillna(method="bfill") # txt360-dedup-only
        txt360_web_up = df.iloc[2:, 7].astype(float).fillna(method="bfill") # txt360-web-only-upsampled
        txt360_all_up_stack = df.iloc[2:, 9].astype(float).fillna(method="bfill") # txt360-all-upsampled + stackv2

        # each row is 20B tokens.
        # all_eval_results[metric_name]["slimpajama"] = slimpajama_res
        all_eval_results[metric_name]["fineweb"] = fineweb_res
        all_eval_results[metric_name]["txt360-dedup-only"] = txt360_base
        all_eval_results[metric_name]["txt360-web-only-upsampled"] = txt360_web_up
        all_eval_results[metric_name]["txt360-all-upsampled + stackv2"] = txt360_all_up_stack
        all_eval_results[metric_name]["token"] = [20 * i for i in range(len(fineweb_res))]


# Eval Result Plots
all_eval_res_figs = {}
for metric_name, res in all_eval_results.items():
    fig_res = go.Figure()

    # Add lines
    fig_res.add_trace(go.Scatter(
        x=all_eval_results[metric_name]["token"], 
        y=all_eval_results[metric_name]["fineweb"], 
        mode='lines', name='FineWeb'
    ))
    fig_res.add_trace(go.Scatter(
        x=all_eval_results[metric_name]["token"], 
        y=all_eval_results[metric_name]["txt360-web-only-upsampled"], 
        mode='lines', name='TxT360 - CC Data Upsampled'
    ))
    fig_res.add_trace(go.Scatter(
        x=all_eval_results[metric_name]["token"], 
        y=all_eval_results[metric_name]["txt360-dedup-only"], 
        mode='lines', name='TxT360 - CC Data Dedup'
    ))
    fig_res.add_trace(go.Scatter(
        x=all_eval_results[metric_name]["token"], 
        y=all_eval_results[metric_name]["txt360-all-upsampled + stackv2"], 
        mode='lines', name='TxT360 - Full Upsampled + Stack V2'
    ))

    print(all_eval_results[metric_name]["token"])
    print(all_eval_results[metric_name]["fineweb"].tolist())
    print(all_eval_results[metric_name]["txt360-web-only-upsampled"].tolist())

    # Update layout
    fig_res.update_layout(
        title=f"{metric_name} Performance",
        title_x=0.5,  # Centers the title
        xaxis_title="Billion Tokens",
        yaxis_title=metric_name,
        legend_title="Dataset",
    )
    all_eval_res_figs[metric_name] = fig_res

##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 (Global)",
    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, 16.57735734574608, 16.186575145468233, 16.043253935667327, 16.63513328173465, 16.39894567786065, 16.41214937220052, 16.493426793298827, 15.9907685927303, 16.340040345074534, 16.951414035127105, 15.885674447160902, 16.410931308490415, 15.964154315977636, 16.810589683350447, 16.745778707293876, 16.468627893278832, 16.138744388702552, 16.407068111214684, 16.69176851966938, 16.324663512055274, 16.433577086199467, 16.434498347983276, 16.70257568394254, 18.747662848672356, 16.321669513149416, 16.69707440155245, 16.900596553383878, 16.68130890408306, 16.59473760593977, 16.54783975654349, 16.88654672752656, 16.078918331814624, 16.299820079993204, 16.373797255734377, 16.866705899661024, 16.397749521656102, 15.963898354324714, 16.82397059101694, 15.93018673258056, 16.98101514795396, 16.159948488588498, 16.726848882847232, 17.07683842070863, 16.113967724590644, 16.15130250991399, 16.80200457395006, 16.497186754611253, 16.666948876534608, 16.27533407301657, 16.308682326270063, 16.49999383144354, 16.359445800006256, 15.96150693135751, 16.60238027701047, 16.852103553279054, 16.521323558607705, 16.892194162833896, 15.693912585511482, 16.941903323105247, 16.480018831423866, 15.994720828335334, 19.96982983642109, 16.5613669780375, 16.410472896702814, 16.930635781928018, 17.307088122382734, 17.141450875814638, 16.793262685672637, 16.898963500457878, 16.034046523941534, 16.535173078962604, 15.623283937530607, 16.9146472987719, 16.710942224491752, 16.986982069138335, 16.094316754584177, 16.828775635709547, 16.087671276732564, 15.85332219694136, 16.48892512748128, 15.947276977857264, 17.071060205585102, 17.024065382461824, 15.924022369577948, 15.155645181021022, 19.43284882900329, 18.262079233670903, 16.72008178909451, 16.191840443840587, 17.202976578718893, 18.073639551374267, 16.211906986032727, 16.893893336751606, 16.459697938511802, 16.340211536130877, 17.323792190608398, 16.166587611972542, 17.10118411108414, 16.87592915776603, 16.22000303371821, 17.099521471855354, 16.65188809044026, 15.439452520219506, 17.1238180228351, 16.822849013604262, 16.573524603379, 17.943366953648695, 18.61078665129674, 16.781687468016873, 16.277476841130934, 15.656708089753856, 16.93898908638033, 17.165185636822365, 16.88121672323756, 16.662421334264874, 16.351884226195278, 16.409706698188405, 17.36934322952571, 16.42350307052468, 16.644262788006284, 18.645487431486863, 17.45592775934621, 16.58861831024693, 17.826687228059075, 17.717259497674252, 16.173776963074275, 17.81471375669573, 17.043108559597915, 17.447724642873848, 17.245380090633933, 17.159851613935718, 17.06408636418627, 17.636831200319357, 17.226401140951793, 16.89171092276326, 17.27705035910921, 16.42925715421899, 17.297253521528557, 17.152355698826447, 17.673683744959348, 18.04475478619425, 16.737975421424565, 16.86577082453459, 16.863767158267972, 16.384443285083982, 16.461263048130785, 17.02229949930192, 17.101654001031108, 17.356582073600375, 16.344481856681604, 17.17676991586537, 17.185069687958347, 17.56978076611228, 17.438405238494422, 16.801320242413205, 17.283593618974137, 15.979660333719643, 17.530455106750154, 16.192472201610578, 17.392028244723697, 17.502518627885713, 17.407844930629604, 16.696324269027805, 16.03847705843595, 16.23747483686068, 18.737873580931968, 17.86589992376309, 17.25159517234638, 15.618977309780984, 17.21762645376281, 16.869727185054728, 16.96866468612357, 17.412146418998862, 18.090503138314943, 15.752757796291071, 18.160108290669523, 16.495087053440795, 17.89917434478209, 16.704112151245884, 14.966161599960373, 18.271963637296526, 17.495885294624035, 16.876079860453665, 18.868793874875955, 17.23085150515865, 16.817181665750674, 17.24528354632844, 16.338842229657274, 14.433218936285867, 17.32290971310563, 17.641663765189666, 16.756405276984953, 18.144689315919752, 15.468462735891627, 14.740977777972642, 17.74261979627107, 17.1782285639448, 15.146783133278753, 17.987671541660372, 17.24769683193406],
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13.670103747374988, 13.62246956240464, 13.658574692934657, 13.65219324836813, 13.663836335892329, 13.666948307267594, 13.642643510414398, 13.672961601819406, 13.663525877548398, 13.667436573958156, 13.687522639036205, 13.647544546546508, 13.670704172336292, 13.643000490240736, 13.667107110344569, 13.660218070172265, 13.675550822990397, 13.59092599672469, 13.619850375757148, 13.651551988902462, 13.684288098100867, 13.599046453546292, 13.580905963181452, 13.620956017533885, 13.648408527460056, 13.60249514150298, 13.568078301644128, 13.6145797181229, 13.647719674647586, 13.531493703586886, 13.609260600121146, 13.687447710013837, 13.602215210547463, 13.563220813507392, 13.57176728376717, 13.664222431811334, 13.586259696651297, 13.571127927324502, 13.692682818016978, 13.599437831977406, 13.646024625160337, 13.645231022004468, 13.582760709524955, 13.597408008025921, 13.640791164279545, 13.58016693386471, 13.664139165839629, 13.553901320176095, 13.616471379535914, 13.574418885444663, 13.62049913139043, 13.595296717779055, 13.618520397945241, 13.619083853308746, 13.549404239296155, 13.515187660214737, 13.728226318739061, 13.618609127026419, 13.638021829042664, 13.684199453984574, 13.707676555543845, 13.541617144639595, 13.703834138276244, 13.519323579997998, 13.6031555746482, 13.56414632339735, 13.711610914557605, 13.529701508251849, 13.631734977501925, 13.657120297408555, 13.58489249685537, 13.64403630157245, 13.608235747585796, 13.512110498832085, 13.846959297419959, 13.488917791046676, 13.696834245583917, 13.61507766454769, 13.565377802829648, 13.790197786574588, 13.564346823139237, 13.5342157764538, 13.65148446769962, 13.570646849759997, 13.538878864721305, 13.608980817839505, 13.664477237928127, 13.532411719398455, 13.737437273256564, 13.519816331488395, 13.764368990604515, 13.653588472445573, 13.5302540795639, 13.57561188810073, 13.647608383912683, 13.728643890026184, 13.710097008284352, 13.75589344505474, 13.682502650080487, 13.684339202206958, 13.86749342599449, 13.645786331176726, 13.660817039716257, 13.638011389586667, 13.732387353692099, 13.687686391049773, 13.94728981311995, 13.614547412507926, 13.460552695752389, 13.857074456574574, 13.660326212827487, 13.718393121459215, 13.76973846040823, 13.778762864123724, 13.656959991670522, 14.214462072816556, 13.622739420457773, 13.645227120727897, 13.687497326712435, 13.649272554141808, 13.503550000252996, 14.052494437028535, 13.580311058963396, 14.14017314736784, 13.64182000911326, 13.886936632170794, 13.942674178813384, 13.801754161915504, 13.931144818346747, 13.584651322907064, 13.960391830857033, 14.178873989248789, 13.645923431288137, 13.97423140921055, 13.669569353257327, 13.915331700422486, 13.827145087821776, 13.736836827537942, 13.74766543270326, 13.770318536193258, 13.721033560006754, 13.710191655241298, 13.72744974500201, 13.845900599098753, 13.678446729208007, 13.902643304888189, 13.885717101405227, 13.800092557910519, 13.421930828385424, 14.22006344809192, 13.879986040380091, 14.134020605148754, 13.828712030750555, 13.713190050100788, 13.670401789346878, 13.948423234333882, 13.84481181587886, 13.818908981670926, 13.74582730003707, 13.94097096205736, 14.180988587482524, 13.792218775776533, 13.684855627228844, 13.881739174231223, 13.998746647008945, 14.576445279868192, 13.977974630190932, 13.537548783758675, 13.793279757360363, 13.712674433009878, 13.758696209000801, 13.912382937440464, 14.340144810498328, 14.03279240397904, 13.889996587251982, 13.862065986515601, 13.712302898403514, 13.797613183843772, 13.847295441275936, 14.422108283887903, 13.900169262027397, 14.247783256416513, 14.02056653491548, 13.99526374998638, 13.69358071050138, 13.828640744267325, 14.01321596237364, 13.824657271537166, 13.683602248925892, 13.933987455146445, 13.993921243916123, 13.720966315955001, 13.890770809341586, 13.426961283787957, 13.789886238259456, 13.904131224083592, 13.697354217113691, 13.710497864728065, 14.683006531539148, 14.315759264484964, 14.034699751802846, 13.890948305458895, 14.038907287423818, 13.298757873288494, 14.037824844116592, 14.162250808384407, 13.947058195824253, 13.806653590867487, 13.980386450979841, 13.667273172120268, 14.201737731565034, 14.447849496372461, 14.018697147254176, 14.002315381014313, 14.15231239942244, 13.73719042552595, 14.033594655653038, 14.03087108296539, 13.782578520604822, 13.660908086958573, 14.205910336717215, 13.930322566343703, 14.300591211697906, 13.808749910964323, 13.8076208806989, 14.169819824542373, 14.067537576689853, 14.306124453553517, 13.56827905088618, 13.592634469105525, 13.789396713937975, 13.853464456609156, 13.855768885291056, 13.710464058237172, 13.641314331273772, 14.295528532894258, 14.04977108393012, 14.153384726345907, 13.385524853965638, 13.484650040422745, 13.831061312642468, 14.15298734516191, 14.20289531849528, 14.116529526532855, 13.767652893233096, 13.715924776449405, 14.649027392365024, 13.603310028324861, 14.10228343978809, 13.704762155010586, 13.731715455443299], 
    },
}

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 (global)",
    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 (local)",
    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 (local)",
    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 (Global)",
    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 (Global)",
    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 (Global)",
    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 (Local)",
    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)),
        "perplexities": [9.73915022954687, 8.920626670584653, 10.794318768046299, 8.843588010546013, 9.050843813116494, 8.930966597941875, 9.037282952268393, 8.861595199889763, 8.753115155540607, 9.750853693230052, 8.684437196297443, 9.220661575409212, 8.779997997083122, 8.395973456433415, 8.73217254093509, 8.881588084417483, 8.631494252144279, 8.819106667105284, 8.896815931063438, 8.659420224734506, 9.093486198999384, 9.696607960142106, 8.508805626033137, 8.770987058526325, 8.618185592039293, 7.915860238031309, 8.83767692043796, 8.727961757725632, 8.485070840013236, 9.764445257512499, 8.600757313466264, 9.081950235300344, 8.644217818204663, 8.55864241676558, 8.233431060928043, 8.184825012856324, 8.457157826105203, 8.306222356999731, 8.804352979539, 8.313005637251672, 9.026721732161297, 8.449083429742776, 8.733235778798473, 9.006749183049818, 8.200737283467852, 8.251897579249375, 7.787355375938481, 7.268723383407269, 7.571889349513068, 8.351626749016013, 8.400107029097354, 7.723260069861529, 8.064236740258659, 7.888157433279341, 8.148191006636575, 7.906415374915906, 7.676551458219022, 7.967158452060384, 7.932665786459153, 7.9763750026305305, 7.667885913458702, 7.839381126838969, 7.92098160290061, 8.028601269205508, 7.563111341601447, 7.6678178481341215, 7.111641341466761, 7.42738813332767, 6.926910421359465, 7.363397686520664, 7.4639497513997775, 7.970748152252673, 7.356418997104175, 7.343504223513313, 6.8766667549967355, 6.358501202955649, 6.424115736059589, 6.008071046749611, 6.639372134895001, 5.6177395770356835, 5.443864799344331, 5.6647930543792, 4.172380654474099, 5.128083927608737, 5.146055825516793, 3.8998212782432287, 2.6866750639386963],
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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 (Local)",
    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. 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: 15 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 consistently outperforms FineWeb after upsampling, particularly on challenging tasks like MMLU and generation tasks such as NQ. Similar to the findings in DCLM, adding non-CommonCrawl data sources produces mixed results, especially when testing with that specific version of the data. We have since updated the non-CC data to further reduce noise."
        ),
        plotly2fasthtml(all_eval_res_figs["MMLU"]),
        plotly2fasthtml(all_eval_res_figs["NQ"]),
        # plotly2fasthtml(all_eval_res_figs["GSM8K"]),
        plotly2fasthtml(all_eval_res_figs["HellaSwag"]),
        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. DataComp-LM 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. Following this practice, we estimated data quality by taking a", A("KenLM model",href="https://huggingface.co/edugp/kenlm"), "trained on English Wikipedia data to compute perplexity on data with different duplication patterns. Lower perplexity is regarded as a signal of higher quality."),
        H3("Sampling Strategy"),
        P("We started from a processed Common Crawl (CC) ablation dataset divided by the number of duplicates of each document. For each CC dump, we have different buckets each holding chunks of document with different duplicate count ranges (1-1, 2-5, 6-10, 11-100, 101-1000, 1001-30000000). We sampled the first 10k documents from each chunk with their meta data."),
)


perp1_div = Div(
        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."),
            #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."),
            #Img(src="images/prep-across-diff-year-global-dup-buckets.png", height = "300", width = "600" ),
            plotly2fasthtml(graph2222),
            
        ),
    Section(
            H3("Perplexity vs Document Duplication"),
            P("We can also break each bucket into distinct document counts. The graph becomes a bit noisy at the end because of insufficient samples with larger duplication counts."),
            #Img(src="images/prep-across-diff-docs-dup-count-global.png", height = "300", width = "600" ),
            plotly2fasthtml(graph3),
        ),
    Section(
            H3("Perplexity vs Dump Duplication"),
            P("We are also interested in how the number of dumps a document is in affect data quality. From the graph below we can see that documents that are duplicated across around 40 - 60 dumps usually have lower perplexity."),
            #Img(src="images/prep-across-diff-dump-dup-counts-global.png", height = "300", width = "600" ),
            plotly2fasthtml(graph4),
        ),
    Section(
            H3("Perplexity vs Local Buckets"),
            P("Previously we have seen that documents in recent dumps 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 by reading the metadata attached with each document."),
            #Img(src="images/prep-across-diff-buckets-local.png", height = "300", width = "600" ),
            plotly2fasthtml(graph5),
        ),
    Section(
            H3("Perplexity vs Local Dump Duplication"),
            P("Following the same practice, we can plot the local version of the graph of average perplexity with respect to dump duplication."),
            #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 Local Buckets"),
            #Img(src="images/prep-diff-buckets-local.png", height = "300", width = "600" ),
            plotly2fasthtml(llama_graph5),
        ),
    Section(
            H3("Perplexity vs Local Dump Duplication"),
            #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("We tried to classify data into topic groups and looked for correlations between topics and statistics of data. Data 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 the ", A("common crawl", href="https://commoncrawl.org/"), " data 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 which include quality signals and other useful 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"),
        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("Results Analysis"),
        *(
            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"
                )