|
|
--- |
|
|
language: en |
|
|
license: mit |
|
|
library_name: transformers |
|
|
pipeline_tag: text-classification |
|
|
tags: |
|
|
- finbert |
|
|
- finance |
|
|
- sentiment-analysis |
|
|
- tech-stocks |
|
|
base_model: ProsusAI/finbert |
|
|
datasets: |
|
|
- financial_phrasebank |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
--- |
|
|
|
|
|
# FinBERT-Tech-Sentiment |
|
|
|
|
|
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) for sentiment analysis on financial news related to major technology companies. |
|
|
|
|
|
## Model Description |
|
|
|
|
|
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...}] |
