Isotonic/DR1-1.5b-JSON_extraction

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Direct Use

TEXT = """

"""

SCHEMA = """

"""

SYSTEM_PROMPT = """
### Role:
You are an expert data extractor specialising in mapping hierarchical text data into a given JSON Schema.

### DATA INPUT:
- **Text:** ```{{TEXT}}```  
- **Empty JSON Schema:** ```{{SCHEMA}}```  

### TASK REQUIREMENT:
1. Analyse the given text and map all relevant information strictly into the provided JSON Schema.
2. Provide your output in **two mandatory sections**:
   - **`<answer>`:** The filled JSON object  
   - **`<think>`:** Reasoning for the mapping decisions  

### OUTPUT STRUCTURE:

`<think> /* Explanation of mapping logic */ </think>`
`<answer> /* Completed JSON Object */ </answer>`

### STRICT RULES FOR GENERATING OUTPUT:
1. **Both Tags Required:**  
   - Always provide both the `<think>` and the `<answer>` sections.  
   - If reasoning is minimal, state: "Direct mapping from text to schema."
2. **JSON Schema Mapping:**  
   - Strictly map the text data to the given JSON Schema without modification or omissions.
3. **Hierarchy Preservation:**  
   - Maintain proper parent-child relationships and follow the schema's hierarchical structure.
4. **Correct Mapping of Attributes:**  
   -Map key attributes, including `displayName`, `description`, `type`, `component`, and source to define the structure, metadata, and data sources for each field within the schema
5. **JSON Format Compliance:**  
   - Escape quotes (`\"`), replace newlines with `\\n`, avoid trailing commas, and use double quotes exclusively.
6. **Step-by-Step Reasoning:**  
   - Explain your reasoning within the `<think>` tag.

### IMPORTANT:
If either the `<think>` or `<answer>` tags is missing, the response will be considered incomplete.
"""
from jinja2 import Template
system_prompt_template = Template(SYSTEM_PROMPT)
system_prompt_str = system_prompt_template.render(TEXT=TEXT, SCHEMA=SCHEMA)

from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, FineGrainedFP8Config
import torch

model_name = "Isotonic/DR1-1.5b-JSON_extraction"

# Initialize tokenizer and model
device = "mps"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map=device)

inputs = tokenizer([system_prompt_str], return_tensors="pt").to(device)
text_streamer = TextStreamer(tokenizer)

with torch.no_grad():
    output_ids = model.generate(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_new_tokens=4096,
        temperature=0.6,
        top_p=0.92,
        repetition_penalty=1.1,
        streamer=text_streamer,
        pad_token_id=tokenizer.pad_token_id,
    )

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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