from fasthtml.common import * from fasthtml.components import * import json from fh_plotly import plotly2fasthtml from plotly import graph_objects as go import pandas as pd import plotly.express as px ##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-1.5T')) 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 plot # 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 #Perplexity Across Different Buckets (global) # The data you provided DATA = [ ["2014", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [17.410227605477868, 16.11176217183986, 15.632757662414805, 15.446116676532212, 16.716943171826703, 18.156821563322765]]], ["2015", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [17.446573602753478, 16.14852530113782, 15.627408549576069, 15.0055028132117, 15.565430373421485, 17.314701050452452]]], ["2016", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [17.307221780905284, 16.297702171159543, 15.948641884223639, 14.799690714225637, 14.935989931859659, 16.09585768919658]]], ["2017", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [17.338525603992114, 15.960924352297502, 15.912187993988933, 14.822102470001267, 14.778913482337416, 15.428145290012955]]], ["2018", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [17.08551151136689, 16.187802102106698, 14.935072408852303, 14.832038213200583, 14.508674264491997, 14.800605964649103]]], ["2019", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [16.818363305107052, 16.474269837858706, 14.944741674400241, 14.568394784374943, 14.690158822673334, 15.990949424635108]]], ["2020", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [16.98821894111693, 15.936494557783181, 14.79960386342691, 14.435682562274105, 14.58651834886038, 15.869365567783806]]], ["2021", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [17.125795647512877, 15.780419457145868, 14.631430892394002, 14.276477514399625, 14.337146941773641, 15.872474774329305]]], ["2022", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [16.573462144306383, 15.283018703313582, 14.378277745163881, 14.0611924390084, 13.9886330091318, 15.769421394877273]]], ["2023", [["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"], [15.4293630385597, 14.608379914730168, 14.118271697056592, 13.880215644749589, 13.767106666731275, 15.05749135510839]]] ] # Extract ranges (buckets) and years ranges = DATA[0][1][0] years = [year_data[0] for year_data in DATA] all_values = [year_data[1][1] for year_data in DATA] # Create the figure fig = go.Figure() # Add a trace for each year for i, year in enumerate(years): values = all_values[i] fig.add_trace(go.Scatter(x=ranges, y=values, mode='lines+markers', name=year)) # Update layout fig.update_layout( title="Perplexity Versus Buckets for Different Years", xaxis_title="Buckets", yaxis_title="Perplexity", legend_title="Years", hovermode="x unified" ) Perplexity_Across_Different_Buckets_global_graph = fig ##graph 2 # Data years = ["2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021", "2022", "2023"] buckets = ["1-1", "2-5", "6-10", "11-100", "101-1000", "1001-30000000"] data = { "2014": [17.410227605477868, 16.11176217183986, 15.632757662414805, 15.446116676532212, 16.716943171826703, 18.156821563322765], "2015": [17.446573602753478, 16.14852530113782, 15.627408549576069, 15.0055028132117, 15.565430373421485, 17.314701050452452], "2016": [17.307221780905284, 16.297702171159543, 15.948641884223639, 14.799690714225637, 14.935989931859659, 16.09585768919658], "2017": [17.338525603992114, 15.960924352297502, 15.912187993988933, 14.822102470001267, 14.778913482337416, 15.428145290012955], "2018": [17.08551151136689, 16.187802102106698, 14.935072408852303, 14.832038213200583, 14.508674264491997, 14.800605964649103], "2019": [16.818363305107052, 16.474269837858706, 14.944741674400241, 14.568394784374943, 14.690158822673334, 15.990949424635108], "2020": [16.98821894111693, 15.936494557783181, 14.79960386342691, 14.435682562274105, 14.58651834886038, 15.869365567783806], "2021": [17.125795647512877, 15.780419457145868, 14.631430892394002, 14.276477514399625, 14.337146941773641, 15.872474774329305], "2022": [16.573462144306383, 15.283018703313582, 14.378277745163881, 14.0611924390084, 13.9886330091318, 15.769421394877273], "2023": [15.4293630385597, 14.608379914730168, 14.118271697056592, 13.880215644749589, 13.767106666731275, 15.05749135510839] } # Create a line plot for each bucket fig = go.Figure() for i, bucket in enumerate(buckets): bucket_values = [data[year][i] for year in years] fig.add_trace(go.Scatter(x=years, y=bucket_values, mode='lines+markers', name=bucket)) # Update layout fig.update_layout( title="Average Perplexity Over Years by Bucket", xaxis_title="Year", yaxis_title="Average Perplexity", legend_title="Buckets", hovermode="x unified" ) # Show plot graph2 = fig #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, 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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], }, } for year, year_data in data.items(): fig.add_trace(go.Scatter( x=year_data['x'], y=year_data['y'], mode='lines+markers', name=f"Year {year}", line_shape='linear' )) # Update layout fig.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 = fig ##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, 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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, 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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, 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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], }, "2015": { "duplicates": list(range(1, 88)), "perplexities": [9.219127989173247, 8.662048439524266, 8.38989056455147, 7.962841520680627, 7.9129221891669, 8.601740522408205, 7.072072698161579, 8.547935092167322, 9.032202360967963, 9.644330594119374, 8.8352494084492, 9.04389212105191, 8.887379409385023, 8.467909602302054, 8.822876185378563, 8.655601459122618, 8.73446662907839, 8.858527575243034, 8.884133792160123, 8.489950876265588, 9.093447553065644, 9.577116330804195, 8.564448599529959, 8.758142067509203, 8.56227523869569, 7.897451823056177, 8.780353272984362, 8.69589044489135, 8.469444871399634, 9.707586741035607, 8.549040139041137, 9.062827976790162, 8.645009864743498, 8.544819434495203, 8.218203612587951, 8.191690418776773, 8.42249826138689, 8.296810827637307, 8.760096580513409, 8.3252124274653, 8.992877987118824, 8.454953276821902, 8.699916032688582, 8.953715077645272, 8.181603571788644, 8.193005628373307, 7.792425191978106, 7.2744004758877745, 7.554966996453596, 8.35034561406835, 8.41512904956328, 7.684576106312188, 8.064672849239695, 7.937226299628162, 8.236727165389356, 7.824011302908751, 7.622715782362401, 7.960707630747855, 7.932794820691164, 7.929912243495168, 7.655015402069077, 7.962802955194318, 7.851307904639461, 7.964993709642837, 7.551712280758451, 7.708432510205161, 7.1666588182507835, 7.3873387045347325, 6.911323642701545, 7.47032697252429, 7.5402879073116535, 7.826497044764086, 7.269655663988105, 7.343193140755126, 6.732955100033813, 6.447813049685099, 6.444597512230532, 6.007330197959276, 6.503017736395001, 5.297028390356502, 5.425460792319903, 5.536870445249572, 4.171727106880239, 5.112414745674864, 5.144884382794889, 3.914611382312465, 2.6866750639386963], }, "2016": { "duplicates": list(range(1, 88)), "perplexities": [9.224890447512712, 8.865209727234555, 7.716537450318067, 8.505567000250219, 8.231186376526843, 8.439303045267778, 7.877163015312243, 7.994466878194956, 8.658354490436203, 8.525931733072895, 9.410755179427637, 9.700628031783868, 8.99451144975165, 9.002303200677753, 9.126870672453386, 9.422070510449645, 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7.152179142613762, 7.400538018976905, 6.9780334804483495, 7.455001719017696, 7.07739603554367, 7.201520909070617, 6.8033512129548726, 6.932923527827607, 6.796367425560507, 7.349719615399371, 7.290546797526088, 7.5224745941917055, 7.234885644016566, 7.373214375228053, 6.746192846100825, 6.23400120737243, 6.33988992018498, 6.030827322485673, 6.526425922427022, 5.371781592457229, 5.52171375482226, 5.521789795055974, 4.281913900699859, 5.135628510388279, 5.198507257987441, 3.9021738986547896, 2.6866750639387633], }, "2020": { "duplicates": list(range(1, 88)), "perplexities": [10.012609650486192, 7.996780616269575, 8.486989186126861, 8.589723559690643, 8.39765295502809, 8.493985763693104, 8.444565685004276, 8.521057974766807, 8.262640776205831, 8.635134709906502, 8.811211245843253, 9.235066189908858, 9.022297426838923, 9.050058439072979, 9.021682564717057, 8.873493708246608, 9.015417293624877, 8.740954222830077, 9.0380883860745, 8.889091357432216, 8.912750014641842, 8.882441443817264, 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6.222443482377916, 6.382107628208513, 5.9634307406816855, 6.574337724456447, 5.299637438768189, 5.445760462611284, 5.536805681860065, 4.174658965854665, 5.1170397769906595, 5.103551563888967, 3.9064523489117278, 2.6866750639387456], }, "2021": { "duplicates": list(range(1, 88)), "perplexities": [9.841089195168713, 8.376002975517325, 8.380380483628153, 8.188986954093957, 8.498635277059357, 8.023999615729231, 8.382569297820478, 8.547323342280805, 8.188133428925276, 8.92217442481792, 9.176245490194058, 9.243284040091787, 9.034153905594078, 9.202098403867247, 9.134743272368441, 8.987006737157861, 8.92449319257734, 8.825574314893755, 8.847995893024844, 8.941295135829169, 8.987413373621635, 8.946047635293768, 8.910627453693433, 8.788838301728244, 8.76786062380576, 8.382956738298825, 8.699642493063092, 8.653055171884096, 8.740110731986059, 8.962375284828505, 8.333692265451546, 8.538371629422324, 7.61125418543177, 8.043172182416328, 8.716508021173402, 8.108788324737684, 7.9519597656706695, 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"perplexities": [9.483592568856565, 7.19946566373024, 6.689435696789969, 7.735695023912755, 8.891994985807424, 7.974675072464074, 8.38103445140788, 7.767845780936468, 8.394625582114237, 8.433832557729854, 8.774679671114042, 8.974690045840946, 8.970427821588581, 9.259164010333704, 9.106573684091558, 9.082142024590542, 9.00694910652822, 8.951939452627803, 8.834410968583011, 8.976270078171723, 8.9788747911979, 9.147340358822175, 8.94766754786145, 8.863791307783835, 8.876434772786448, 8.537925507030351, 8.824632718943558, 8.670890722043863, 8.713248499915446, 8.991496303395792, 8.497388391287544, 8.694429166461068, 7.660306866593211, 8.063103914688337, 8.463798472858477, 8.147225054222934, 7.98918792448372, 8.129552622440363, 8.156552658413169, 7.993772322199681, 8.077300760319622, 7.687003596840271, 8.216734207325837, 7.818779874173861, 7.784873713139422, 7.841502120166083, 8.009056410739252, 7.643507378655463, 7.027652318922246, 7.578018517117392, 8.21806831598481, 7.738109238050354, 7.657938120144026, 7.670918897910402, 7.789453032753468, 7.6784988295976015, 7.758997892938832, 7.354548111787801, 7.324563987410127, 7.542453103467507, 7.082203068171782, 7.41432116627747, 6.902549671764324, 7.489234680249967, 7.354679134462809, 6.977235447939879, 7.297608588934183, 7.439224077591953, 7.01074155919995, 7.77157588876759, 7.452660719169145, 7.206525557457038, 6.837175958650314, 6.816276605521482, 6.841636273605858, 6.4126962333247475, 6.544729510231483, 5.84044063139556, 6.384392701668377, 5.342064638285962, 5.449806194943077, 5.550638967579051, 4.188085941944831, 5.167854549955437, 5.137630179031516, 3.901881866936028, 2.686675063938701], }, "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-1.5T": [ "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)", ], } ) table_html = dataset_comparison.to_html(index=False, border=0) table_div_1 = Div(NotStr(table_html), style="margin: 40px;") intro_div = Div( H2("TxT360 Results"), H3("What This Section Contains"), P("This section provides brief upsampling ablation examples and detailed perplexity analysis of CommonCrawl snapshots. The section is split into the following topic areas: "), Ul( Li("Upsampling Experiment: TxT360 vs FineWeb", style = "margin-bottom: 5px"), Li("Perplexity Evaluation on Duplicate Data", style = "margin-bottom: 5px"), ), ) upsampling_exp = Div( H2("Upsampling Experiment: TxT360 vs FineWeb"), H3("Experiment Setup"), P("We performed a comparison of 1.5T tokens from FineWeb and 1.5T tokens of TxT360 across 10 diverse evaluations. Our FineWeb evaluation is based on a random sample 1.5T tokens from FineWeb (base). For TxT360, we also random sample 1.5T tokens by upsampling data instances with more duplicates. Concretely, the upsampling weight is set to 3 for data points with duplicates in the range from 2 to 5, 5 for the range from 5 to 100, 8 for that from 101 to 1000, and 10 for more than 1000 duplicates."), table_div_1, P("To account for differing dataset sizes, the evaluation scores represent the final evaluation score after the entire dataset has been processed."), H3("Training Evaluations"), P("We also conducted full scale training using TxT360 and FineWeb-1.5T. Below are plots of the training and validation loss curves for each dataset. We can see that TxT360 achieves a lower training and validation loss compared to FineWeb-1.5T. "), plotly2fasthtml(validation_loss_graph), plotly2fasthtml(lm_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 [DataComp-LM](https://arxiv.org/abs/2406.11794), which used perplexity filtering as a candidate for quality filtering. DataComp-LM followed [CCNet’s](https://arxiv.org/abs/1911.00359) practice to use a 5-gram Kneser-Ney model as implemented in the [KenLM](https://github.com/kpu/kenlm) library for efficient perplexity calculation. Following this practice, we estimated data quality by taking a KenLM model (from [edugp/kenlm](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(graph2), P("NEED TO UPDATE - THIS GRAPH SHOULD MATCH THE IMAGE ABOVE AND YEAR SHOULD NOT BE a LINE OPTION"), ), 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), P("NEED TO UPDATE - THIS GRAPH SHOULD MATCH THE IMAGE ABOVE AND BUCKET SHOULD NOT BE a LINE OPTION"), ), 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), ), ) def results(): return Div( Section( intro_div, upsampling_exp, preplexity_intro_div, perp1_div, llama_div, id="inner-text" ) )