metadata

license: mit
language:
  - en

Structure Extraction Model by NuMind 🔥

NuExtract is a fine-tuned version of phi-3-mini, on a private high-quality syntatic dataset for information extraction. To use the model, provide an input text (less than 2000 tokens) and a JSON schema describing the information you need to extract.

Note: This model is purely extractive, so each information output by the model is present as it is in the text. You can also provide an example of output to help the model understand your task more precisely.

try here: https://huggingface.co/spaces/numind/NuExtract

Checkout other models by NuMind:

SOTA Zero-shot NER Model NuNER Zero
SOTA Multilingual Entity Recognition Foundation Model: link
SOTA Sentiment Analysis Foundation Model: English, Multilingual

Benchmark

Benchmark 0 shot (will release soon):

Benchmark fine-tunning:

Usage

To use the model:

import json
from transformers import AutoModelForCausalLM, AutoTokenizer


def predict_NuExtract(model,tokenizer,text, schema,example = ["","",""]):
    schema = json.dumps(json.loads(schema), indent=4)
    input_llm =  "<|input|>\n### Template:\n" +  schema + "\n"
    for i in example:
      if i != "":
          input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\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("numind/NuExtract", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract", trust_remote_code=True)

#model.to("cuda")

model.eval()

text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""

schema = """{
    "Model": {
        "Name": "",
        "Number of parameters": "",
        "Number of token": "",
        "Architecture": []
    },
    "Usage": {
        "Use case": [],
        "Licence": ""
    }
}"""

prediction = predict_NuExtract(model,tokenizer,text, schema,example = ["","",""])
print(prediction)