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fine-tuned-bert-financial-sentiment-analysis
stfamod/fine-tuned-bert-financial-sentiment-analysis
fine-tuned-bert-financial-sentiment-analysis is a text classification model from stfamod. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
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 financialphrasebank and auditorsentiment datasets.
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Sep 1, 2023
- Updated
- Sep 2, 2023
- SHA
- d83be641
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Model card
From the Hugging Face model README
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}]
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Sep 1, 2023
- Updated
- Sep 2, 2023