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

In this tutorial, we build a complete PDF-to-structured-data extraction workflow around Lift, with a focus on controlled evaluation rather than a simple demo run. We begin by preparing a Colab-compatible GPU environment, selecting the appropriate precision mode for the available hardware, and patching model loading to ensure the Lift backend runs reliably even on constrained 16 GB GPUs via 4-bit NF4 quantization. From there, we generate synthetic multi-page research reports with deliberately placed distractors, including validation-versus-test metric ambiguity, baseline-versus-proposed-model comparisons, missing code-release cases, and boolean state-of-the-art claims. This provides a realistic testbed for schema-guided extraction, in which the model must recover titles, authors, datasets, metrics, hyperparameters, limitations, and repository links from document layouts rather than plain text.

Configuring Runtime and Dependencies

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N_DOCS = 3 FORCE_FULL_PRECISION = False FORCE_4BIT = False SHOW_FIRST_PAGE = True RUN_ON_REAL_PDF = False REAL_PDF_URL = "https://arxiv.org/pdf/1512.03385" REAL_PDF_PAGES = "0-3" PIN_PILLOW = True PILLOW_VERSION = "11.3.0" import os, sys, subprocess, json, re, time, warnings warnings.filterwarnings("ignore") os.environ["TOKENIZERS_PARALLELISM"] = "false" def pip(*pkgs, upgrade=False): """Install without invoking a shell (so '[hf]' is never glob-expanded).""" args = [sys.executable, "-m", "pip", "install", "-q"] + (["-U"] if upgrade else []) + list(pkgs) print(" pip install", *pkgs) subprocess.run(args, check=False) print("STEP 1/7 · Installing lift + light dependencies (first run is the slow one)…") pip("reportlab", "pypdfium2", "pandas", "matplotlib") pip("lift-pdf[hf]") pip("bitsandbytes", "accelerate", upgrade=True) if PIN_PILLOW: pip(f"pillow=={PILLOW_VERSION}") if "PIL" in sys.modules: import PIL if getattr(PIL, "version", "") != PILLOW_VERSION: print(f" Pinned Pillow {PILLOW_VERSION} on disk, but a stale Pillow " f"({getattr(PIL, 'version', '?')}) is already loaded in memory.") print(" Restarting the runtime now — just re-run the cell(s) after it reconnects.") os.kill(os.getpid(), 9) print(" …install finished.\n") import torch

We configure the tutorial runtime by defining the main execution knobs for corpus size, precision mode, preview rendering, and optional real-PDF extraction. We also install the core dependencies required for PDF generation, rendering, plotting, and Lift’s Hugging Face backend. The Pillow pinning logic is important because it prevents a known Colab compatibility issue in which newer Pillow builds can break downstream imports via torchvision and transformers.

Loading Lift 4-bit Backend

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def detect_gpu(): if not torch.cuda.is_available(): raise SystemExit( "\n✗ No CUDA GPU found. In Colab: Runtime ▸ Change runtime type ▸ GPU " "(A100 is best; L4/T4 also work).\n" ) p = torch.cuda.get_device_properties(0) cc = torch.cuda.get_device_capability(0) return p.name, p.total_memory / 1e9, cc def enable_4bit(compute_dtype): """ Load lift's weights in 4-bit NF4 no matter which transformers Auto* class it uses internally. We inject a quantization_config + on-GPU device_map, and neutralize any later model.to()/.cuda() (which is illegal on a bnb-quantized model). This is what lets a ~10 B model fit on a 16 GB T4 / 24 GB L4. """ import inspect, functools, transformers from transformers import BitsAndBytesConfig bnb = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=compute_dtype, ) def patch(cls): try: cm = inspect.getattr_static(cls, "from_pretrained") orig = cm.func if isinstance(cm, (classmethod, staticmethod)) else cm except Exception: return @functools.wraps(orig) def inner(cls_, *args, kwargs): kwargs.setdefault("quantization_config", bnb) kwargs.setdefault("device_map", {"": 0}) model = orig(cls_, *args, kwargs) try: model.to = lambda *a, k: model model.cuda = lambda *a, k: model except Exception: pass return model cls.from_pretrained = classmethod(inner) for name in ["AutoModelForImageTextToText", "AutoModelForMultimodalLM", "AutoModelForVision2Seq", "AutoModelForCausalLM", "AutoModel"]: c = getattr(transformers, name, None) if c is not None: patch(c) try: from transformers.modeling_utils import PreTrainedModel patch(PreTrainedModel) except Exception: pass print("STEP 2/7 · Preparing the model backend…") gpu_name, vram, cc = detect_gpu() use_4bit = FORCE_4BIT or (vram = 8 else torch.float16 print(f" GPU: {gpu_name} | ~{vram:.0f} GB | compute capability {cc[0]}.{cc[1]}") print(f" Load mode: {'4-bit NF4' if use_4bit else 'full bf16'} (compute dtype {compute_dtype})") os.environ.setdefault("TORCH_DEVICE", "cuda:0") os.environ.setdefault("MODEL_CHECKPOINT", "datalab-to/lift") if use_4bit: enable_4bit(compute_dtype) from lift import extract from lift.model import InferenceManager print(" Loading lift weights (≈20 GB download on first run)…") _t = time.time() MODEL = InferenceManager(method="hf") print(f" ✓ model ready in {time.time() - _t:.0f}s\n") def run_lift(pdf_path, schema, page_range=None): kw = {"model": MODEL} if page_range: kw["page_range"] = page_range result = extract(pdf_path, schema, **kw) return getattr(result, "extraction", None)

We prepare the Lift inference backend by detecting available CUDA GPUs, estimating VRAM usage, and choosing between full-precision and 4-bit NF4 loading. The 4-bit patch injects a BitsAndBytes quantization configuration into compatible Transformers model loaders, allowing the model to fit on smaller GPUs such as T4 or L4. We then initialize a reusable InferenceManager that avoids reloading the model for each document and makes the extraction pipeline practical for batch processing.

Building the Synthetic Corpus

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DOCS = [ dict( title="SolarNet: Efficient Land-Cover Classification from Multispectral Satellite Imagery", authors=[("Maya Okafor", "TU Delft"), ("Liang Wei", "TU Delft"), ("Priya Ramachandran", "European Space Research Institute")], task="satellite image land-cover classification", method="SolarNet", datasets=["EuroSAT", "BigEarthNet", "So2Sat"], primary_benchmark="EuroSAT", metric_name="Top-1 accuracy", test_acc=96.4, val_acc=97.1, baseline_name="ResNet-50", baseline_val=92.0, baseline_test=91.2, params_m=42.7, optimizer="AdamW", lr=0.0003, batch=128, epochs=90, beats_sota=True, prior_best=95.1, code_url=None, funding_note="This work was supported by the Open Earth Initiative. " "The authors do not release source code for the trained models.", limitations=["Accuracy degrades on scenes with heavy cloud cover.", "Trained only on imagery at 10 m spatial resolution."], ), dict( title="GraphMoE: Mixture-of-Experts Message Passing for Molecular Property Prediction", authors=[("Sofia Álvarez", "ETH Zürich"), ("Daniel Kim", "ETH Zürich"), ("Yara Haddad", "Genentech"), ("Tom Becker", "ETH Zürich")], task="molecular property prediction", method="GraphMoE", datasets=["OGB-MolHIV", "QM9", "ZINC"], primary_benchmark="OGB-MolHIV", metric_name="ROC-AUC", test_acc=0.812, val_acc=0.828, baseline_name="GIN", baseline_val=0.784, baseline_test=0.771, params_m=8.3, optimizer="Adam", lr=0.001, batch=256, epochs=120, beats_sota=True, prior_best=0.799, code_url="https://github.com/mol-ai/graphmoe", funding_note="Funded by the Swiss NSF. Code and pretrained checkpoints are available " "at https://github.com/mol-ai/graphmoe.", limitations=["Expert routing adds ~15% inference latency versus a dense GNN.", "Evaluated only on small-molecule datasets under 50 heavy atoms."], ), dict( title="AcoustiFormer: A Compact Transformer for Environmental Sound Classification", authors=[("Noah Fischer", "University of Edinburgh"), ("Aisha Bello", "University of Edinburgh"), ("Kenji Watanabe", "Sony CSL")], task="environmental sound classification", method="AcoustiFormer", datasets=["ESC-50", "UrbanSound8K"], primary_benchmark="ESC-50", metric_name="accuracy", test_acc=88.7, val_acc=90.3, baseline_name="CNN14", baseline_val=90.8, baseline_test=89.2, params_m=22.1, optimizer="AdamW", lr=0.0005, batch=64, epochs=200, beats_sota=False, prior_best=89.2, code_url="https://github.com/audio-lab/acoustiformer", funding_note="Code available at https://github.com/audio-lab/acoustiformer.", limitations=["A larger CNN baseline still outperforms our model on ESC-50.", "Performance was not evaluated on real-time streaming audio."], ), ][:N_DOCS] def ground_truth(d): """Reshape a source dict into the exact JSON shape our schema asks for.""" return { "title": d["title"], "authors": [{"name": n, "affiliation": a} for (n, a) in d["authors"]], "primary_task": d["task"], "proposed_method_name": d["method"], "datasets": d["datasets"], "headline_metric": {"name": d["metric_name"], "value": d["test_acc"], "benchmark": d["primary_benchmark"]}, "num_parameters_millions": d["params_m"], "hyperparameters": {"optimizer": d["optimizer"], "learning_rate": d["lr"], "batch_size": d["batch"], "epochs": d["epochs"]}, "beats_prior_sota": d["beats_sota"], "code_url": d["code_url"], "limitations": d["limitations"], }

We define a small but carefully controlled synthetic corpus of machine-learning research reports with structured metadata. Each document includes realistic fields such as authors, datasets, benchmark metrics, hyperparameters, model size, code availability, limitations, and SOTA claims. The ground_truth function reshapes the same source metadata into the exact JSON structure expected by the extraction schema, providing a precise reference for evaluation.

Rendering Multi-Page PDF Reports

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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["e

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