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--- |
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license: mit |
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tags: |
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- sentiment |
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- sentiment-analysis |
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- financial |
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- fine-tuned |
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- fine-tuned-bert |
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- bert-uncased |
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--- |
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### Model Overview: |
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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. It achieves the following accuracies in the trained datasets: |
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**financial_phrasebank accuracy:** 0.993 |
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**auditor_senitment accuracy:** 0.974 |
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### Training Hyperparameters: |
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**Learning Rate:** 2e-05 |
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**Train Batch Size:** 16 |
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**Eval Batch Size:** 16 |
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**Random Seed:** 42 |
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**Optimizer:** AdamW-betas(0.9, 0.999) |
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**Learning Rate Scheduler:** Linear |
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**Number of Epochs:** 6 |
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**Number of Warmup Steps:** 0.2 * Number of Training Steps |
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### How To Use: |
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``` |
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>> from transformers import pipeline |
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>> pipe = pipeline("sentiment-analysis", model="mstafam/fine-tuned-bert-financial-sentimental-analysis") |
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>> text = "Example company has seen a 5% increase in revenue this quarter." |
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>> pipe(text) |
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[{'label': 'Positive', 'score': 0.9993795156478882}] |
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``` |
