• bitcoinBitcoin(BTC)$65,473.00-0.70%
  • ethereumEthereum(ETH)$1,923.12-0.20%
  • tetherTether(USDT)$1.000.00%
  • binancecoinBNB(BNB)$569.46-0.20%
  • usd-coinUSDC(USDC)$1.000.00%
  • rippleXRP(XRP)$1.13-0.50%
  • solanaSolana(SOL)$77.570.00%
  • tronTRON(TRX)$0.327146-0.90%
  • Figure HelocFigure Heloc(FIGR_HELOC)$1.00-0.30%
  • whitebitWhiteBIT Coin(WBT)$57.23-0.30%
  • HyperliquidHyperliquid(HYPE)$59.230.40%
  • dogecoinDogecoin(DOGE)$0.072264-0.50%
  • RainRain(RAIN)$0.014251-3.10%
  • USDSUSDS(USDS)$1.000.00%
  • leo-tokenLEO Token(LEO)$9.73-0.10%
  • zcashZcash(ZEC)$516.520.30%
  • moneroMonero(XMR)$347.04-0.20%
  • cardanoCardano(ADA)$0.1745490.40%
  • chainlinkChainlink(LINK)$8.62-0.30%
  • stellarStellar(XLM)$0.184585-2.70%
  • CantonCanton(CC)$0.120219-2.90%
  • daiDai(DAI)$1.000.00%
  • bitcoin-cashBitcoin Cash(BCH)$216.16-2.60%
  • USD1USD1(USD1)$1.000.00%
  • the-open-networkGram (prev. Toncoin)(GRAM)$1.510.60%
  • Ethena USDeEthena USDe(USDE)$1.000.00%
  • litecoinLitecoin(LTC)$46.830.80%
  • Global DollarGlobal Dollar(USDG)$1.000.00%
  • hedera-hashgraphHedera(HBAR)$0.0729162.20%
  • suiSui(SUI)$0.770.70%
  • Circle USYCCircle USYC(USYC)$1.13-0.10%
  • avalanche-2Avalanche(AVAX)$6.581.00%
  • paypal-usdPayPal USD(PYUSD)$1.000.00%
  • crypto-com-chainCronos(CRO)$0.0584520.40%
  • BlackRock USD Institutional Digital Liquidity FundBlackRock USD Institutional Digital Liquidity Fund(BUIDL)$1.000.00%
  • tether-goldTether Gold(XAUT)$4,078.91-0.60%
  • shiba-inuShiba Inu(SHIB)$0.000004-0.60%
  • nearNEAR Protocol(NEAR)$1.89-0.10%
  • uniswapUniswap(UNI)$3.811.30%
  • Ondo US Dollar YieldOndo US Dollar Yield(USDY)$1.140.20%
  • World Liberty FinancialWorld Liberty Financial(WLFI)$0.06646614.30%
  • OndoOndo(ONDO)$0.402160-2.10%
  • BittensorBittensor(TAO)$196.15-0.50%
  • pax-goldPAX Gold(PAXG)$4,077.27-0.60%
  • okbOKB(OKB)$84.373.40%
  • AsterAster(ASTER)$0.630.40%
  • HTX DAOHTX DAO(HTX)$0.0000020.20%
  • Ripple USDRipple USD(RLUSD)$1.000.00%
  • usddUSDD(USDD)$1.000.00%
  • MemeCoreMemeCore(M)$1.15-2.60%
TradePoint.io
  • Main
  • AI & Technology
  • Stock Charts
  • Market & News
  • Business
  • Finance Tips
  • Trade Tube
  • Blog
  • Shop
No Result
View All Result
TradePoint.io
No Result
View All Result

Using Lift to Turn Research PDFs into Structured JSON with Controlled, Schema-Guided Field-Level Evaluation

July 1, 2026
in AI & Technology
Reading Time: 3 mins read
A A
Using Lift to Turn Research PDFs into Structured JSON with Controlled, Schema-Guided Field-Level Evaluation
ShareShareShareShareShare

YOU MAY ALSO LIKE

Uber Lays Off 10 Percent Of Its Customer Service Team In Favor Of Using AI

Best Open Speech Recognition (ASR) Models in 2026: WER, Languages, Latency, and License Compared

def render_pdf(d, path):
   """Draw a realistic 3-page report. Page breaks are forced so the headline metric on
   page 1 (abstract) is physically separated from the results table on page 3."""
   from reportlab.lib.pagesizes import LETTER
   from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
   from reportlab.lib.units import inch
   from reportlab.lib import colors
   from reportlab.platypus import (SimpleDocTemplate, Paragraph, Spacer,
                                   Table, TableStyle, PageBreak)
   ss = getSampleStyleSheet()
   H1   = ParagraphStyle("H1", parent=ss["Title"], fontSize=16, leading=20, spaceAfter=6)
   AUTH = ParagraphStyle("AUTH", parent=ss["Normal"], fontSize=9.5, textColor=colors.grey, spaceAfter=10)
   H2   = ParagraphStyle("H2", parent=ss["Heading2"], fontSize=12, spaceBefore=8, spaceAfter=4)
   BODY = ParagraphStyle("BODY", parent=ss["Normal"], fontSize=10, leading=14, spaceAfter=6)
   sota_phrase = (f"surpassing the previous best of {d['prior_best']}"
                  if d["beats_sota"] else
                  f"approaching but not exceeding the previous best of {d['prior_best']}")
   authors_line = ", ".join(f"{n} ({a})" for (n, a) in d["authors"])
   story = []
   story += [Paragraph(d["title"], H1), Paragraph(authors_line, AUTH), Paragraph("Abstract", H2)]
   story += [Paragraph(
       f"We introduce {d['method']}, a model for {d['task']}. On the {d['primary_benchmark']} "
       f"benchmark, {d['method']} attains {d['test_acc']} {d['metric_name']} on the held-out "
       f"test set, {sota_phrase}. Our {d['params_m']}M-parameter model is evaluated across "
       f"{len(d['datasets'])} datasets ({', '.join(d['datasets'])}). "
       f"Extensive ablations confirm the contribution of each component.", BODY)]
   story += [Paragraph("Keywords", H2),
             Paragraph(f"{d['task']}; representation learning; {d['primary_benchmark']}", BODY),
             PageBreak()]
   story += [Paragraph("1  Method and Training Details", H2)]
   story += [Paragraph(
       f"{d['method']} is trained end-to-end with the {d['optimizer']} optimizer. "
       f"We tune on a validation split and report final numbers on the test split. "
       f"The full training configuration is summarized in Table 1.", BODY)]
   hp = [["Hyperparameter", "Value"],
         ["Optimizer", d["optimizer"]],
         ["Learning rate", str(d["lr"])],
         ["Batch size", str(d["batch"])],
         ["Epochs", str(d["epochs"])],
         ["Parameters", f"{d['params_m']}M"]]
   t1 = Table(hp, colWidths=[2.4 * inch, 2.0 * inch])
   t1.setStyle(TableStyle([
       ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#2b3a67")),
       ("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
       ("FONTSIZE", (0, 0), (-1, -1), 9.5),
       ("GRID", (0, 0), (-1, -1), 0.4, colors.grey),
       ("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.white, colors.HexColor("#eef1f8")]),
       ("LEFTPADDING", (0, 0), (-1, -1), 8), ("TOPPADDING", (0, 0), (-1, -1), 4),
       ("BOTTOMPADDING", (0, 0), (-1, -1), 4)]))
   story += [Spacer(1, 4), t1, Spacer(1, 6),
             Paragraph("Table 1. Training configuration.", BODY),
             Paragraph("2  Datasets", H2),
             Paragraph(
                 f"We evaluate on {', '.join(d['datasets'])}. {d['primary_benchmark']} is our "
                 f"primary benchmark; the remaining datasets are used for generalization "
                 f"studies.", BODY),
             PageBreak()]
   story += [Paragraph("3  Results", H2)]
   res = [["Method", f"Val. {d['metric_name']}", f"Test {d['metric_name']}"],
          [f"{d['baseline_name']} (baseline)", str(d["baseline_val"]), str(d["baseline_test"])],
          [f"{d['method']} (ours)", str(d["val_acc"]), str(d["test_acc"])]]
   t2 = Table(res, colWidths=[2.6 * inch, 1.7 * inch, 1.7 * inch])
   t2.setStyle(TableStyle([
       ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#7a2e2e")),
       ("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
       ("FONTSIZE", (0, 0), (-1, -1), 9.5),
       ("GRID", (0, 0), (-1, -1), 0.4, colors.grey),
       ("FONTNAME", (0, 2), (-1, 2), "Helvetica-Bold"),
       ("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.white, colors.HexColor("#f7eeee")]),
       ("LEFTPADDING", (0, 0), (-1, -1), 8), ("TOPPADDING", (0, 0), (-1, -1), 4),
       ("BOTTOMPADDING", (0, 0), (-1, -1), 4)]))
   story += [Spacer(1, 4), t2, Spacer(1, 6),
             Paragraph(f"Table 2. Results on {d['primary_benchmark']}. "
                       f"Best test result in bold.", BODY),
             Paragraph("4  Limitations", H2)]
   for lim in d["limitations"]:
       story += [Paragraph("• " + lim, BODY)]
   story += [Paragraph("5  Funding and Code Availability", H2),
             Paragraph(d["funding_note"], BODY)]
   SimpleDocTemplate(path, pagesize=LETTER,
                     topMargin=0.8 * inch, bottomMargin=0.8 * inch,
                     leftMargin=0.9 * inch, rightMargin=0.9 * inch).build(story)
print("STEP 3/7 · Generating synthetic report PDFs…")
CORPUS = []
for i, d in enumerate(DOCS):
   path = f"/content/report_{i}.pdf" if os.path.isdir("/content") else f"report_{i}.pdf"
   render_pdf(d, path)
   CORPUS.append((d, ground_truth(d), path))
   print(f"     ✓ {os.path.basename(path)}  —  {d['method']}")
print()
if SHOW_FIRST_PAGE:
   try:
       import pypdfium2 as pdfium, matplotlib.pyplot as plt
       pg  = pdfium.PdfDocument(CORPUS[0][2])[0]
       img = pg.render(scale=2.0).to_pil()
       plt.figure(figsize=(6.4, 8.3)); plt.imshow(img); plt.axis("off")
       plt.title("What lift reads — page 1 of report_0.pdf", fontsize=10); plt.show()
   except Exception as e:
       print("     (page preview skipped:", e, ")\n")

Credit: Source link

ShareTweetSendSharePin

Related Posts

Uber Lays Off 10 Percent Of Its Customer Service Team In Favor Of Using AI
AI & Technology

Uber Lays Off 10 Percent Of Its Customer Service Team In Favor Of Using AI

July 23, 2026
Best Open Speech Recognition (ASR) Models in 2026: WER, Languages, Latency, and License Compared
AI & Technology

Best Open Speech Recognition (ASR) Models in 2026: WER, Languages, Latency, and License Compared

July 23, 2026
A SpaceX Falcon 9 Rocket Is Expected To Crash Into The Moon This August
AI & Technology

A SpaceX Falcon 9 Rocket Is Expected To Crash Into The Moon This August

July 23, 2026
Meet Gigatoken: A Rust BPE Tokenizer that Encodes Text at 24.53 GB/s, up to 989x Faster than HuggingFace Tokenizers
AI & Technology

Meet Gigatoken: A Rust BPE Tokenizer that Encodes Text at 24.53 GB/s, up to 989x Faster than HuggingFace Tokenizers

July 23, 2026
Next Post
Thousands evacuated after gas facility explosion in Mexico

Thousands evacuated after gas facility explosion in Mexico

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Search

No Result
View All Result
How Patience Pays Off in Trading

How Patience Pays Off in Trading

July 22, 2026
Meet Gigatoken: A Rust BPE Tokenizer that Encodes Text at 24.53 GB/s, up to 989x Faster than HuggingFace Tokenizers

Meet Gigatoken: A Rust BPE Tokenizer that Encodes Text at 24.53 GB/s, up to 989x Faster than HuggingFace Tokenizers

July 23, 2026
Paddleboarders rescue raccoon from Texas lake

Paddleboarders rescue raccoon from Texas lake

July 23, 2026

About

Learn more

Our Services

Legal

Privacy Policy

Terms of Use

Bloggers

Learn more

Article Links

Contact

Advertise

Ask us anything

©2020- TradePoint.io - All rights reserved!

Tradepoint.io, being just a publishing and technology platform, is not a registered broker-dealer or investment adviser. So we do not provide investment advice. Rather, brokerage services are provided to clients of Tradepoint.io by independent SEC-registered broker-dealers and members of FINRA/SIPC. Every form of investing carries some risk and past performance is not a guarantee of future results. “Tradepoint.io“, “Instant Investing” and “My Trading Tools” are registered trademarks of Apperbuild, LLC.

This website is operated by Apperbuild, LLC. We have no link to any brokerage firm and we do not provide investment advice. Every information and resource we provide is solely for the education of our readers. © 2020 Apperbuild, LLC. All rights reserved.

No Result
View All Result
  • Main
  • AI & Technology
  • Stock Charts
  • Market & News
  • Business
  • Finance Tips
  • Trade Tube
  • Blog
  • Shop

© 2023 - TradePoint.io - All Rights Reserved!