This model was fine-tuned on a filtered subset of the financial_phrasebank dataset. Specifically, it was trained on sentences from the Sentences_50Agree.txt file that contained keywords for major tech companies (Google, Apple, Microsoft, Amazon, Nvidia, etc.).

Due to the very small size of the filtered dataset (58 samples), this model is intended as a proof-of-concept and its performance is limited.

Evaluation Results

The model achieves the following results on the evaluation set:

Loss: 2.3548
Accuracy: 0.25
F1 Score: 0.2639

How to Use

You can use this model directly with the pipeline function from the transformers library.

python

from transformers import pipeline

sentiment_analyzer = pipeline(
    "text-classification",
    model="esix117/FinBERT-Tech-Sentiment"
)

# Example 1
result = sentiment_analyzer("Nvidia reported record earnings, beating all estimates.")
print(result)
# >> [{'label': 'positive', 'score': 0.8...}]

# Example 2
result = sentiment_analyzer("Apple shares fell after the new product announcement.")
print(result)
# >> [{'label': 'negative', 'score': 0.9...}]

assix-research/FinBERT-Tech-Sentiment

FinBERT-Tech-Sentiment

Model Description

Evaluation Results

How to Use

assix-research/
FinBERT-Tech-Sentiment