redphoenix1996/srs_ambiguity_generation_1

# Ambiguity Type Classification Model

Overview

This repository contains an ambiguity type classification model fine-tuned on [dataset name]. The model is designed to classify text into different types of ambiguity, helping to identify and categorize ambiguous language in various contexts.

Model Details

• Model Architecture: GPT
• Base Model: LLaMA-3 8B
• Fine-tuned on: Custom Dataset
• Number of Classes: 7
• Performance Metrics:
  • Accuracy: [99]%
  • F1 Score: [96]

Ambiguity Types

The model classifies text into the following ambiguity types:

1. Semantic Ambiguity,
2. Syntactic Ambiguity,
3. Functional Ambiguity,
4. Operational Ambiguity,
5. Scope Ambiguity,
6. Temporal Ambiguity,
7. Quality Ambiguity

Usage

Here's a quick example of how to use the model:

from transformers import AutoModelForCausalLM, AutoTokenizer 


# Replace 'your_hugging_face_username/your_model_name' with your actual repository name repository_id = "your_hugging_face_username/your_model_name" 


# Load the model and tokenizer 
model = AutoModelForCausalLM.from_pretrained(repository_id) 
tokenizer = AutoTokenizer.from_pretrained(repository_id) 


# Test the loaded model and tokenizer 
input_text = "Hello, how are you?" 
inputs = tokenizer(input_text, return_tensors="pt") 
outputs = model.generate(**inputs) 
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(generated_text)