|
--- |
|
license: mit |
|
tags: |
|
- sentiment |
|
- sentiment-analysis |
|
- financial |
|
- fine-tuned |
|
- fine-tuned-bert |
|
- bert-uncased |
|
--- |
|
|
|
### Model Overview: |
|
This NLP model is fine-tuned with a focus on analyzing sentiment in financial text and news headlines. It was trained using the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on the [financial_phrasebank](https://huggingface.co/datasets/financial_phrasebank) and [auditor_sentiment](https://huggingface.co/datasets/FinanceInc/auditor_sentiment) datasets. |
|
|
|
**Accuracies:** |
|
**financial_phrasebank accuracy:** 0.993\ |
|
**auditor_senitment accuracy:** 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}] |
|
``` |
