mstafam/fine-tuned-bert-financial-sentiment-analysis

Model Overview:

This NLP model is fine-tuned with a focus on analyzing sentiment in financial text and news headlines. It was fine-tuned using the bert-base-uncased model on the financial_phrasebank and auditor_sentiment datasets.

Accuracies:
financial_phrasebank: 0.993
auditor_senitment: 0.974

Training Hyperparameters:

Learning Rate: 2e-05
Train Batch Size: 16
Eval Batch Size: 16
Random Seed: 42
Optimizer: AdamW-betas(0.9, 0.999)
Learning Rate Scheduler: Linear
Number of Epochs: 6
Number of Warmup Steps: 0.2 * Number of Training Steps

How To Use:

from transformers import pipeline
pipe = pipeline("sentiment-analysis", model="mstafam/fine-tuned-bert-financial-sentimental-analysis")

text = "Example company has seen a 5% increase in revenue this quarter."

print(pipe(text))

[{'label': 'Positive', 'score': 0.9993795156478882}]