finsentiment-deberta-v3-base

Fine-tuned microsoft/deberta-v3-base for three-class financial sentiment classification (negative / neutral / positive) on a combined corpus of:

• Financial PhraseBank (via the nickmuchi/financial-classification mirror)
• Twitter Financial News Sentiment (zeroshot/twitter-financial-news-sentiment)

Test macro-F1: 0.876 (accuracy 0.892, ECE 0.034).

Evaluation

Metric	Fine-tuned	FinBERT (zero-shot)
macro-F1	0.876	0.731
accuracy	0.892	0.765
F1 (negative)	0.858	0.661
F1 (neutral)	0.914	0.819
F1 (positive)	0.856	0.712
Expected Calibration Error	0.034	0.075

Evaluation was run on a stratified 10% test split of the combined corpus (seed 42), with ProsusAI/finbert as a zero-shot domain-pretrained baseline.

How to use

from transformers import pipeline

clf = pipeline("text-classification", model="anabdd/finsentiment-deberta-v3-base", top_k=None)
clf("Apple beat earnings expectations on iPhone strength.")
# → [{'label': 'positive', 'score': 0.97}, ...]

Training details

Trained with Trainer (HuggingFace transformers >= 4.46) using:

• Class-weighted cross-entropy loss (sklearn balanced weighting on the training split)
• Label smoothing (factor 0.05)
• Cosine LR schedule with 10% warmup
• Mixed-precision (fp16) on a single NVIDIA T4 GPU

See the project repository for the full training script (src/train.py) and the analysis notebook.

Intended use & limitations

Designed for short-form financial English text (headlines, tweets, brief analyst notes). It has not been trained for:

• Languages other than English
• Long-form financial documents (10-Ks, earnings call transcripts)
• Fine-grained sentiment beyond three classes

Labels follow the nickmuchi/financial-classification mirror's convention, which is itself derived from the original PhraseBank annotation scheme.