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NebuIA/
nebuia_extract_small

Name: nebuia_extract_small
Availability: InStock

text-generation-inference

endpoints_compatible

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Structure Extraction Model

nebuia_extract_small is an extraction model inspired by NuExtract. nebuia_extract_small is a version of qween 1.5b, fine-tuned on a private high-quality synthetic dataset for entity extraction in Spanish legal texts with an 8k context length. Supports JSON template like nu extract describing the information you need to extract. NebuIA Extract specializes in identifying and extracting legal entities and relevant information from Spanish legal documents.

Model Details

Model Description

Developed by: NebuIA
Language(s) (NLP): es
License: mit
Finetuned from model [optional]: Qween2 1.5b

Uses

Same template as NuExtract

python

import json
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch


def predict_extract(model, tokenizer, text, schema):
    schema = json.dumps(json.loads(schema), indent=4)
    input_llm =  "<|input|>\n### Template:\n" +  schema + "\n"
    
    input_llm +=  "### Text:\n"+text +"\n<|output|>\n"
    input_ids = tokenizer(input_llm, return_tensors="pt", truncation=True, max_length=4000).to("cuda")

    output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
    return output.split("<|output|>")[1].split("<|end-output|>")[0]


model = AutoModelForCausalLM.from_pretrained("NebuIA/nebuia_extract_small", trust_remote_code=True, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("NebuIA/nebuia_extract_small", trust_remote_code=True)

model.to("cuda")

model.eval()

text = """large legal text"""

schema = """{
    "calusulas": [],
    "notario": "",
    "jurisdiccion": {
      "clausula_jurisdiccion": "",
      "lugar": ""
    }
}"""

prediction = predict_extract(model, tokenizer, text, schema)
print(prediction)