mhtccc/ernie-4-5-0.3B-finance-sft
The mhtccc/ernie-4-5-0.3B-finance-sft is a 0.3 billion parameter language model, fine-tuned from Baidu's ERNIE-4.5-0.3B-PT base model. This model is specifically optimized for financial sentiment analysis, demonstrating a final validation loss of 0.4226 on the finance_sentiment dataset. Its compact size and specialized training make it suitable for efficient deployment in financial applications requiring sentiment classification.
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Model Overview
The mhtccc/ernie-4-5-0.3B-finance-sft is a compact 0.3 billion parameter language model, fine-tuned from the baidu/ERNIE-4.5-0.3B-PT base model. This specialization focuses on financial sentiment analysis, making it a targeted solution for understanding sentiment within financial texts.
Key Capabilities
- Financial Sentiment Analysis: The model has been fine-tuned on a
finance_sentimentdataset, indicating its primary capability is to classify or understand sentiment in financial contexts. - Compact Size: With 0.3 billion parameters, it is a relatively small model, which can be beneficial for deployment in resource-constrained environments or for applications requiring fast inference times.
- Performance: Achieved a final validation loss of 0.4226 during its training on the specified dataset.
Training Details
The model was trained for 5 epochs using an AdamW optimizer with a learning rate of 5e-05 and a cosine learning rate scheduler with a warmup ratio of 0.1. Batch sizes were set to 4 for both training and evaluation. The training process involved 12,500 steps, showing a consistent reduction in validation loss over epochs.
Good For
- Financial Text Analysis: Ideal for tasks such as analyzing news articles, social media posts, or reports related to finance to gauge market sentiment.
- Edge Deployment: Its small parameter count makes it suitable for deployment on devices with limited computational resources.
- Quick Sentiment Inference: Can provide rapid sentiment classifications for real-time financial data streams.