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--- |
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license: gemma |
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--- |
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This is Gemma 2B model (GGUF Q8_0) fine-tuned for financial aspect based sentiment analysis with structured JSON response. |
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**Input** |
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``` |
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financial_text = """Upon auditing Non-Profit Org, it was clear that the organization has made strides in improving financial accountability and donor transparency. |
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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. |
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""" |
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``` |
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**Response** |
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```json |
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{ |
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"Overall_Sentiment": "Mixed", |
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"Positive_Aspect": [ |
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"financial accountability", |
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"donor transparency" |
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], |
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"Negative_Aspect": [ |
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"fund allocation inefficiencies", |
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"need for financial oversight" |
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] |
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} |
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``` |
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### Get started with Langchain |
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```python |
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#!pip install --upgrade llama-cpp-python langchain_core langchain_community |
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from langchain_community.llms import LlamaCpp |
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from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler |
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# Callbacks support token-wise streaming |
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) |
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llm = LlamaCpp( |
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model_path="Gemma-2B-it-finance-aspect-based-sentiment-gguf-Q8_0.gguf", |
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max_tokens=2048, |
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temperature=0, |
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top_p=1, |
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callback_manager=callback_manager, |
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verbose=True, # Verbose is required to pass to the callback manager |
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) |
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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. |
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### Instruction: |
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Perform Aspect based sentiment analysis. |
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Present your response in python JSON format with "Overall_Sentiment", "Positive_Aspect", "Negative_Aspect". |
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### Input: |
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{financial_text} |
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### Response: |
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""" |
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financial_text = """Upon auditing Non-Profit Org, it was clear that the organization has made strides in improving financial accountability and donor transparency. |
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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. |
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""" |
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prompt = prompt_template.format(financial_text=financial_text) |
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response = llm.invoke(prompt) |
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``` |
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### Response: |
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```json |
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{ |
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"Overall_Sentiment": "Mixed", |
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"Positive_Aspect": [ |
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"financial accountability", |
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"donor transparency" |
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], |
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"Negative_Aspect": [ |
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"fund allocation inefficiencies", |
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"need for financial oversight" |
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] |
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} |
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``` |
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