Base model: ProsusAI/finbert
Training data: FinancialPhraseBank (sentences_allagree, 2,264 sentences) + FiQA-SA (pauri32/fiqa-2018, 1,173 sentences) — 80/10/10 stratified split
Epochs: 5 · Batch size: 16 · LR: 2e-5 · WeightedTrainer (inverse class frequency)
Hardware: Apple MPS (~4.5 min)
Framework: HuggingFace Transformers 4.41

Usage

python

from transformers import pipeline

pipe = pipeline(
    "text-classification",
    model="EomaxlSam/finbert-finetuned-sentimentpulse",
)

pipe("Apple beats Q1 earnings estimates by 12%")
# [{'label': 'positive', 'score': 0.94}]

pipe("Goldman Sachs fell sharply after issuing a guidance cut.")
# [{'label': 'negative', 'score': 0.91}]

Labels

ID	Label
0	negative
1	neutral
2	positive

Repository

github.com/Eomaxl/SentimentPulse