FedDeBERTa

FedDeBERTa is a fine-tuned microsoft/deberta-v3-base model for binary sentiment classification of Federal Reserve communications (FOMC statements, minutes, and related monetary-policy text). It classifies a sentence as expressing a Positive (dovish / improving economic assessment) or Negative (hawkish / deteriorating economic assessment) tone.

This is the BASE variant, fine-tuned directly from the general-purpose deberta-v3-base checkpoint (no domain-adaptive pretraining). A companion model, FedDeBERTa-DAPT, applies continued domain-adaptive pretraining on Federal Reserve text before fine-tuning on the same task.

Both models were developed as part of the dissertation "Domain Adaptive Pretraining for Federal Reserve Sentiment Analysis: A Systematic Study of Small-Corpus Adaptation, Knowledge Distillation, and Cross-Bank Transfer" by Christopher S. Bennett, University of Arkansas at Little Rock.

⚠️ Disclaimer

This is an academic research artifact released alongside a dissertation. It is not intended as financial or investment advice, and outputs should not be used as the sole basis for trading, investment, or policy decisions. Performance figures below reflect a held-out academic test set and may not generalize to other time periods, institutions, or communication styles. Use in any production or decision-making context is at the deployer's own risk.

Model details

Base architecture	DebertaV2ForSequenceClassification (DeBERTa-v3-base backbone)
Hidden size	768
Layers / attention heads	12 / 12
Tokenizer	SentencePiece (Unigram), 128,001 vocabulary entries
Labels	0: Negative, 1: Positive
Dropout (attention / hidden)	0.05 / 0.05
License	Apache-2.0

A note on vocab_size: this model's config.json reports vocab_size: 128100, and its embedding matrix has shape (128100, 768) — 99 rows larger than the tokenizer's actual 128,001-entry vocabulary. This is an inherited quirk from the upstream microsoft/deberta-v3-base checkpoint (99 reserved/unused embedding rows that no token id can ever reach) rather than a bug specific to this fine-tune; it was verified empirically before release and has no effect on model behavior. See REPRODUCIBILITY.md in the companion GitHub repo for details.

Training data

Fine-tuned on a labeled corpus of Federal Reserve communication sentences (source file referenced internally as FED_prelabelled_sent_fixed.csv), with each sentence labeled Positive or Negative for economic-assessment tone. Full dataset construction and labeling methodology are described in the dissertation.

Evaluation

Evaluated on a frozen, grouped stratified 80/20 held-out test split (seed=42, N=1,322: 718 Negative / 604 Positive), verified independently against archived model predictions and cross-checked row-by-row against the frozen split manifest (100% match).

Metric	Value
Weighted F1	81.19%
Accuracy	81.24%
Negative — precision / recall / F1	0.8133 / 0.8496 / 0.8311
Positive — precision / recall / F1	0.8112 / 0.7682 / 0.7891

Compared to the DAPT variant, this BASE model performs within statistical noise of the DAPT model on this test set (ΔF1-weighted = +0.16pp favoring DAPT, 95% CI [-1.44, +1.76]pp, Holm-corrected p = 1.000 — not statistically significant). See the dissertation and REPRODUCIBILITY.md for the full statistical methodology (McNemar's test, Holm-Bonferroni correction, bootstrap CIs).

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("cbenne23/FedDeBERTa")
model = AutoModelForSequenceClassification.from_pretrained("cbenne23/FedDeBERTa")
model.eval()

text = "The Committee judges that the risks to the outlook for economic activity are weighted to the downside."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    logits = model(**inputs).logits
pred_id = torch.argmax(logits, dim=-1).item()
print(model.config.id2label[pred_id])  # "Negative"

Checkpoint integrity

model.safetensors SHA-256: aef5deed2088793e9a416ca7ea7c89ee9d1bd01d9c64886f41a9e82c7af6a188

Citation

If you use this model, please cite the dissertation:

@phdthesis{bennett_fed_sentiment,
  author = {Bennett, Christopher S.},
  title  = {Domain Adaptive Pretraining for Federal Reserve Sentiment Analysis: A Systematic Study of Small-Corpus Adaptation, Knowledge Distillation, and Cross-Bank Transfer},
  school = {University of Arkansas at Little Rock},
  year   = {2026}
}