fin Bert_10k

Introduction

finBert_10k is a specialized model designed to summarize 10-K documents, crucial for investment management. The model processes text input and provides summarized outputs, fine-tuned specifically for financial news summaries.

Architecture

The architecture of finBert_10k is based on the BERT model, adapted for financial text processing. It leverages deep learning techniques to understand and condense the complex language found in financial documents.

Training

finBert_10k was trained using a corpus of financial news and 10-K documents to optimize its performance in summarizing financial texts accurately. The training process involved fine-tuning BERT to focus on financial language nuances.

Guide: Running Locally

To run finBert_10k locally, follow these steps:

1. Install Dependencies: Ensure you have Python and the necessary libraries installed. You can use pip to install required packages.

   pip install transformers torch
   

2. Clone the Repository: Download the model files from the repository.

   git clone https://github.com/Shivam29rathore/finBert_10k.git
   cd finBert_10k
   

3. Load the Model: Use the Hugging Face Transformers library to load the model.

   from transformers import BertTokenizer, BertForSequenceClassification
   tokenizer = BertTokenizer.from_pretrained('Shivam29rathore/finBert_10k')
   model = BertForSequenceClassification.from_pretrained('Shivam29rathore/finBert_10k')
   

4. Run Inference: Input your text and obtain summaries.

   inputs = tokenizer("Your text here", return_tensors="pt")
   outputs = model(**inputs)
   

Suggested Cloud GPUs

For improved performance and reduced processing time, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The finBert_10k project is released under the MIT License, which allows for reuse with minimal restrictions. Users are free to use, modify, and distribute the software, provided the original license terms are retained.