snrspeaks/Gemma-2B-it-Finance-Aspect-Based-Sentiment-Analyzer

This is Gemma 2B model (GGUF Q8_0) fine-tuned for financial aspect based sentiment analysis with structured JSON response.

Input

financial_text = """Upon auditing Non-Profit Org, it was clear that the organization has made strides in improving financial accountability and donor transparency. 
However, the audit also unveiled significant inefficiencies in fund allocation, signaling a need for better financial oversight to ensure the organization's sustainability and mission effectiveness.
"""

Response

{
    "Overall_Sentiment": "Mixed",
    "Positive_Aspect": [
        "financial accountability",
        "donor transparency"
    ],
    "Negative_Aspect": [
        "fund allocation inefficiencies",
        "need for financial oversight"
    ]
}

Get started with Langchain

#!pip install --upgrade llama-cpp-python langchain_core langchain_community

from langchain_community.llms import LlamaCpp
from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler

# Callbacks support token-wise streaming
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

llm = LlamaCpp(
    model_path="Gemma-2B-it-finance-aspect-based-sentiment-gguf-Q8_0.gguf",
    max_tokens=2048,
    temperature=0,
    top_p=1,
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
)

prompt_template = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
Perform Aspect based sentiment analysis. 
Present your response in python JSON format with "Overall_Sentiment", "Positive_Aspect", "Negative_Aspect".


### Input:
{financial_text}


### Response:
"""

financial_text = """Upon auditing Non-Profit Org, it was clear that the organization has made strides in improving financial accountability and donor transparency. 
However, the audit also unveiled significant inefficiencies in fund allocation, signaling a need for better financial oversight to ensure the organization's sustainability and mission effectiveness.
"""

prompt = prompt_template.format(financial_text=financial_text)

response = llm.invoke(prompt)

Response:

{
    "Overall_Sentiment": "Mixed",
    "Positive_Aspect": [
        "financial accountability",
        "donor transparency"
    ],
    "Negative_Aspect": [
        "fund allocation inefficiencies",
        "need for financial oversight"
    ]
}