FedDeBERTa-DAPT
FedDeBERTa-DAPT 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), classifying a sentence's economic-assessment tone as Positive (optimistic — language suggesting economic strength, growth, or confidence in the outlook) or Negative (pessimistic — language suggesting economic weakness, decline, or concern about conditions).
Sentiment is not the same task as monetary-policy stance. Stance classification asks whether policy is easing or tightening (dovish vs. hawkish); sentiment, as used here, asks only whether the text characterizes economic conditions positively or negatively. The two are related — markets sometimes read expressed economic concern as a signal of anticipated easing, and expressed optimism as a signal of anticipated tightening — but sentiment is not a policy vote and is not a substitute for stance. This model was trained and evaluated on sentiment labels only; it does not predict monetary-policy stance.
This is the DAPT (Domain-Adaptive Pretraining) variant: before task fine-tuning, the backbone underwent continued masked-language-model pretraining on a Federal Reserve communications corpus, then was fine-tuned on the same labeled sentiment task as the companion FedDeBERTa BASE model. (Exact DAPT pretraining corpus size, steps, and MLM configuration are documented in the dissertation methods chapter — not restated here to avoid restating unverified figures from memory; can be added on request.)
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, domain-adaptively pretrained) |
| Hidden size | 768 |
| Layers / attention heads | 12 / 12 |
| Tokenizer | SentencePiece (Unigram), 128,001 vocabulary entries — byte-identical to the BASE model's tokenizer (verified via SHA-256) |
| Labels | 0: Negative, 1: Positive |
| Dropout (attention / hidden) | 0.1 / 0.1 |
| License | Apache-2.0 |
A note on vocab_size: unlike the BASE model, this checkpoint's config.json reports vocab_size: 128001, exactly matching the tokenizer's embedding matrix shape (128001, 768). The continued-pretraining step resized the embedding matrix down from the upstream 128,100-row buffer to the tokenizer's actual vocabulary size. Both models are internally consistent and were verified via a live forward-pass smoke test before release; see REPRODUCIBILITY.md in the companion GitHub repo for details.
Training data
Fine-tuned on the same labeled corpus of Federal Reserve communication sentences as the BASE model (source file referenced internally as FED_prelabelled_sent_fixed.csv), with each sentence labeled Positive or Negative for economic-assessment tone. Full dataset construction, domain-adaptive pretraining corpus, and labeling methodology are described in the dissertation.
Evaluation
Evaluated on the same frozen, grouped stratified 80/20 held-out test split as the BASE model (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 | 81.35% |
| Accuracy | 81.39% |
| Negative — precision / recall / F1 | 0.8164 / 0.8482 / 0.8320 |
| Positive — precision / recall / F1 | 0.8108 / 0.7732 / 0.7915 |
Compared to the BASE variant, this DAPT model shows a small point-estimate improvement (ΔF1-weighted = +0.16pp) that is not statistically significant at this sample size (95% CI [-1.44, +1.76]pp, Holm-corrected p = 1.000, McNemar's test). Framed accurately: DAPT is the point-estimate leader on this test set, not a statistically superior model. See the dissertation and REPRODUCIBILITY.md for the full statistical methodology.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("cbenne23/FedDeBERTa-DAPT")
model = AutoModelForSequenceClassification.from_pretrained("cbenne23/FedDeBERTa-DAPT")
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: 2883b6ed9507278c7ff9da9359b6c81e50de15135a56537b739ba6fb2d98c574
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}
}