---
library_name: transformers
language:
- ko
- en
pipeline_tag: text-generation
---

# Meta-Llama-3-8B-Instruct-category_24k-sft-lora

## Model Details

Meta-Llama-3-8B-Instruct-category_24k-sft-lora is an advanced text generation AI model created by 2digit specifically tailored for news analysis purposes.

**Category Identification**: Meta-Llama-3-8B-Instruct-category_24k-sft-lora excels in categorizing news articles into predefined categories. This feature aids in efficiently organizing news content, facilitating easy access to articles relevant to specific topics of interest. The labeled category list includes: publicity, incidents, key figures within companies, market trends, contracts, social activities, collaborations, business ventures, events, opinions, disclosures, bonds, and interest rates.

**Advantages**:

* **Efficiency**: It automates the categorization process, saving time and effort for users involved in news analysis.
* **Precision**: Its sophisticated algorithms ensure accurate categorization, reducing the likelihood of misclassification and providing reliable results.
* **Versatility**: Suitable for a wide range of professionals, including financial analysts, journalists, market researchers, and investors, it adapts to various needs within the realm of news analysis.

Meta-Llama-3-8B-Instruct-category_24k-sft-lora emerges as a robust solution for enhancing the efficiency and accuracy of news analysis processes, catering to the diverse needs of professionals across different industries.



## License

Use of this model requires company approval. Please contact AI@2digit.io. For more details, please refer to the website below: https://2digit.io/#contactus



## Dataset

The model was trained on an internal dataset from 2digit, consisting of 15k dataset.

|   size | description                     |
|-------:|---------------------------------|
| 24,605 | Human labeling of 13 categories |



## Evaluation

We measured model accuracy through an internal evaluation system.

| task     | accuracy | description                  |
|----------|---------:|------------------------------|
| category |     0.72 | News category classification |