---
license: cc0-1.0
base_model: answerdotai/ModernBERT-base
library_name: transformers
pipeline_tag: token-classification
tags:
- funding-extraction
- arxiv
- scholarly-communication
- span-extraction
- modernbert
language:
- en
datasets:
- cometadata/arxiv-pdf-only-works-funding-statement-extraction-train-test
---

# ModernBERT-base Span-Head — Funding Statement Extraction

A custom span-extraction head on top of `answerdotai/ModernBERT-base`. Given a
chunk of an academic paper (up to 8,192 tokens), it predicts the start and end
token positions of a funding statement, plus a "no-answer" probability for
documents with no funding statement.

This is the **rough-extraction stage** of a two-stage cascade:

1. **Stage 1 (this model)**: ModernBERT-base + span head — finds the rough
   span (≈ best@0.85 F1 0.95 on the test set).
2. **Stage 2 (separate)**: `cometadata/funding-cleaning-qwen3-4b-lora` —
   cleans the rough span into the canonical, normalized funding statement
   (strips LaTeX markers, joins paragraph breaks, etc.).

Use this model alone if you only need approximate localization; chain with the
cleanup LoRA if you need the cleaned canonical text.

## Architecture

The architecture is a custom `SpanHead` module (included in `modeling.py`):

```python
import torch
import torch.nn as nn
from transformers import AutoModel


class SpanHead(nn.Module):
    """ModernBERT encoder + start/end/no-answer heads."""

    def __init__(self, base="answerdotai/ModernBERT-base"):
        super().__init__()
        self.encoder = AutoModel.from_pretrained(base)
        h = self.encoder.config.hidden_size  # 768
        self.start_head = nn.Linear(h, 1)
        self.end_head = nn.Linear(h, 1)
        self.no_answer_head = nn.Linear(h, 1)
        self.dropout = nn.Dropout(0.1)

    def forward(self, input_ids, attention_mask):
        out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
        hidden = self.dropout(out.last_hidden_state)
        start_logits = self.start_head(hidden).squeeze(-1)
        end_logits = self.end_head(hidden).squeeze(-1)
        # Mean-pool for no-answer
        mask = attention_mask.unsqueeze(-1).float()
        pooled = (out.last_hidden_state * mask).sum(1) / mask.sum(1).clamp(min=1)
        no_answer = self.no_answer_head(pooled).squeeze(-1)
        return start_logits, end_logits, no_answer
```

## Use

```python
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
from modeling import SpanHead  # bundled in this repo

REPO = "cometadata/funding-extraction-modernbert-base-spanhead"
device = "cuda"

tokenizer = AutoTokenizer.from_pretrained(REPO)
model = SpanHead("answerdotai/ModernBERT-base").to(device)
state_dict = torch.load(
    hf_hub_download(REPO, "pytorch_model.bin"),
    map_location=device, weights_only=True,
)
model.load_state_dict(state_dict)
model.eval()

# `chunk_text` should be a ≤8192-token chunk of the paper (e.g., the
# acknowledgments-containing region). For long papers, run the model on
# sliding 8192-tok windows (stride 4096) and pick the chunk with the lowest
# no-answer probability.

enc = tokenizer(chunk_text, return_offsets_mapping=True,
                 add_special_tokens=False, truncation=True, max_length=8192)
ids = torch.tensor(enc["input_ids"]).unsqueeze(0).to(device)
attn = torch.ones_like(ids)

with torch.no_grad():
    with torch.amp.autocast("cuda", dtype=torch.bfloat16):
        start_logits, end_logits, no_answer = model(ids, attn)

start_logits = start_logits.squeeze(0).float().cpu()
end_logits = end_logits.squeeze(0).float().cpu()
no_answer_prob = torch.sigmoid(no_answer).item()

if no_answer_prob >= 0.5:
    pred_span = ""  # this chunk has no funding statement
else:
    start = int(start_logits.argmax())
    # Constrain end to be after start and within ~300 tokens
    end_window = end_logits[start:start + 300]
    end = start + int(end_window.argmax())
    offsets = enc["offset_mapping"]
    char_s = offsets[start][0]
    char_e = offsets[end][1]
    pred_span = chunk_text[char_s:char_e].strip()
```

## Training data

Built from the 2,384 training rows of
`cometadata/arxiv-pdf-only-works-funding-statement-extraction-train-test`.

For each positive doc (1,416 rows):
- Tokenize `vlm_markdown` with the ModernBERT tokenizer.
- Locate the gold funding statement in `vlm_markdown` via verbatim substring,
  or via `rapidfuzz.partial_ratio_alignment` if not verbatim. Convert
  char-span to token-span.
- Pick the 8,192-token sliding window (stride 4,096) that contains the gold
  span fully. If the doc is ≤ 8,192 tokens, use the whole doc as one chunk.
- Training labels: `start_tok` and `end_tok` indices within the chunk;
  `no_answer = 0`.

For each negative doc (968 rows):
- Use the last 8,192-token chunk of the doc (since funding statements, when
  they exist, are typically near the end).
- Training labels: `start_tok = end_tok = 0`; `no_answer = 1`.

About ~5% of positive rows where no fuzzy alignment ≥ 0.7 could be found are
dropped. Final training set: ~3,300 chunks.

## Loss

```
loss = CE(start_logits[no_answer==0], gold_start)
     + CE(end_logits[no_answer==0], gold_end)
     + 1.0 * BCE_with_logits(no_answer_logit, no_answer_label)
```

The start/end CE is masked out on negative chunks; the no-answer BCE is
computed on all chunks. Padded positions in `start_logits`/`end_logits` are
masked to `-1e4` so they can't be argmax'd.

## Hyperparameters

- Base: `answerdotai/ModernBERT-base` (149M, 8,192-token context)
- Optimizer: AdamW, lr 5e-5, weight decay 0.01
- Schedule: linear warmup (30 steps) + cosine decay
- Epochs: 4
- Batch: 4 per device × 4 grad accum = 16 effective
- Mixed precision: bfloat16
- Max sequence: 8,192 tokens
- Trained on 1× H100 80GB

## Evaluation

On the 597-row test split of
`cometadata/arxiv-pdf-only-works-funding-statement-extraction-train-test`.
At inference we ran this model on the top-2 chunks selected by a separate
ModernBERT-base chunk classifier (binary funding-yes, mean-pooled
classification head) and picked the chunk with the lower no-answer prob.

| Metric                                | Precision | Recall | F1     | F0.5   |
|---------------------------------------|-----------|--------|--------|--------|
| Binary detection                      | 0.9887    | 0.9510 | 0.9694 | 0.9809 |
| Strict span (`token_sort_ratio≥0.95`) | 0.7365    | 0.7084 | 0.7222 | 0.7307 |
| Loose span (max-of-4 fuzz ≥ 0.85)     | 0.9745    | 0.9373 | 0.9556 | 0.9668 |

**Hard ceiling note**: ~28% of test gold statements are not verbatim
substrings of any source representation in the dataset (the dataset's labels
were normalized by frontier models — whitespace, LaTeX markers, paragraph
joins). The 0.95 strict threshold is unforgiving of those normalizations even
on perfectly extracted source-spans, so strict F1 is capped near 0.73 for any
single-stage extractive model. The loose-span F1 of 0.96 is closer to the
practical extractive ceiling.

For higher strict F1, chain with `cometadata/funding-cleaning-qwen3-4b-lora`
which cleans the rough span into the canonical text.

## Cascade pipeline

For long papers (> 8,192 tokens), use a chunk-classifier first to pick the
chunk most likely to contain the funding statement:

```python
# Pseudocode for the full cascade
chunks = sliding_windows(doc, max_tok=8192, stride=4096)
chunk_probs = [chunk_classifier(c) for c in chunks]
top_chunk = chunks[argmax(chunk_probs)]
rough_span = spanhead_model(top_chunk)        # this model
clean_span = cleanup_lora(rough_span, top_chunk)  # other model
```

A simple heuristic alternative to the chunk classifier (also works fine):
just use the last 8,192-token window of the document — funding statements are
usually near the end. This loses a few percentage points of recall on papers
with funding info mid-document.

## Intended use

Extraction of the **rough span** containing a funding acknowledgment from
arXiv paper text (or similar academic markdown). Designed to be the first
stage of a two-stage cascade with the cleanup LoRA, but usable on its own if
you only need approximate localization.

Not intended for: classification of funding sources, downstream
funder/grant/scheme parsing, or extraction from non-paper text.

## Limitations

- Trained on arXiv-derived PDFs only; behavior on other paper sources is
  untested.
- Outputs a rough span — for canonical, downstream-ready text, chain with the
  cleanup LoRA.
- Will occasionally pick the wrong sibling sentence when an acknowledgments
  section contains multiple funding statements (each person's own grants);
  this is the dominant failure mode of the strict-F1 evaluation.

## Citation / acknowledgement

Trained as part of an applied research cycle on the
`cometadata/arxiv-pdf-only-works-funding-statement-extraction-train-test`
dataset by Comet.