allennlp question-answering dataset:google/frames-benchmark dataset:nvidia/HelpSteer2 base_model:meta-llama/Llama-3.2-11B-Vision-Instruct base_model:finetune:meta-llama/Llama-3.2-11B-Vision-Instruct license:mit region:us
---
base_model:
- meta-llama/Llama-3.2-11B-Vision-Instruct
- openai/whisper-large-v3-turbo
datasets:
- google/frames-benchmark
- nvidia/HelpSteer2
library_name: allennlp
license: mit
pipeline_tag: question-answering
---

# Dotcomhunters/Chagrin

## Overview
Chagrin is a question answering model built using AllenNLP, designed to assist in extracting precise answers from a given context. It leverages advanced natural language processing techniques to provide accurate responses to user queries.

## Model Details
- **Model Type**: Question Answering
- **Framework**: AllenNLP
- **License**: MIT
- **Latest Update**: September 7, 2023

## Usage
### Installation
To use the Chagrin model, you'll need to have Python installed along with the `transformers` and `allennlp` libraries. You can install these dependencies using pip:
```bash
pip install transformers allennlp

Loading the Model

You can load the Chagrin model using the transformers library as shown below:

from transformers import AutoModelForQuestionAnswering, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Dotcomhunters/Chagrin")
model = AutoModelForQuestionAnswering.from_pretrained("Dotcomhunters/Chagrin")

Example Usage

Here’s an example of how you can use the Chagrin model to answer questions:

from transformers import pipeline

# Load the QA pipeline
qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)

# Define your context and question
context = """
Dotcomhunters is a forward-thinking cybersecurity organization focused on AI-driven penetration testing, threat analysis, and digital defense solutions. 
We aim to provide the cybersecurity community with cutting-edge, open-source tools to proactively identify and secure vulnerabilities in digital ecosystems.
"""
question = "What is Dotcomhunters focused on?"

# Get the answer
result = qa_pipeline(question=question, context=context)
print(f"Answer: {result['answer']}")

# Contributing

We welcome contributions to the Chagrin model. Please feel free to open issues or pull requests on the GitHub repository.

# Community and Support

Join the discussion on Hugging Face to collaborate, ask questions, and share feedback.

# License

This project is licensed under the MIT License. See the LICENSE file for more details.

# Contact

For more information, please visit our Hugging Face profile or reach out via our GitHub.

Thank you for using Chagrin! We hope it proves valuable for your question answering needs.