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README.md
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README.md
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# NuExtract-1.5_a13714748337156096101495
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---
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license: mit
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language:
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- multilingual
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tags:
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- nlp
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base_model: microsoft/Phi-3.5-mini-instruct
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pipeline_tag: text-generation
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inference: true
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---
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NuExtract-1.5
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# NuExtract-v1.5 by NuMind 🔥
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NuExtract-v1.5 is a fine-tuning of [Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct), trained on a private high-quality dataset for structured information extraction. It supports long documents and several languages (English, French, Spanish, German, Portuguese, and Italian).
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To use the model, provide an input text and a JSON template describing the information you need to extract.
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Note: This model is trained to prioritize pure extraction, so in most cases all text generated by the model is present as is in the original text.
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Check out the [blog post](https://numind.ai/blog/nuextract-1-5---multilingual-infinite-context-still-small-and-better-than-gpt-4o).
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Try it here: [Playground](https://huggingface.co/spaces/numind/NuExtract-v1.5)
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We also provide a tiny (0.5B) version which is based on Qwen2.5-0.5B: [NuExtract-tiny-v1.5](https://huggingface.co/numind/NuExtract-tiny-v1.5)
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⚠️ We recommend using NuExtract with a temperature at or very close to 0. Some inference frameworks, such as Ollama, use a default of 0.7 which is not well suited to pure extraction tasks.
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## Benchmark
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Zero-shot performance (English):
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<p align="left">
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<img src="english_bench.png" style="height: auto;">
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</p>
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Zero-shot performance (Multilingual):
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<p align="left">
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<img src="multilingual_bench.png" style="height: auto;">
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</p>
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Long documents (8-10k tokens):
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<p align="left">
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<img src="8-10_long_context.png" style="height: auto;">
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</p>
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Very long documents (10-20k tokens):
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<p align="left">
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<img src="10-20_long_context.png" style="height: auto;">
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</p>
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Few-shot fine-tuning:
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<p align="left">
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<img src="fewshot_bench.png" style="height: auto;">
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</p>
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## Usage
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To use the model:
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```python
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import json
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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def predict_NuExtract(model, tokenizer, texts, template, batch_size=1, max_length=10_000, max_new_tokens=4_000):
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template = json.dumps(json.loads(template), indent=4)
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prompts = [f"""<|input|>\n### Template:\n{template}\n### Text:\n{text}\n\n<|output|>""" for text in texts]
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outputs = []
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with torch.no_grad():
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for i in range(0, len(prompts), batch_size):
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batch_prompts = prompts[i:i+batch_size]
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batch_encodings = tokenizer(batch_prompts, return_tensors="pt", truncation=True, padding=True, max_length=max_length).to(model.device)
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pred_ids = model.generate(**batch_encodings, max_new_tokens=max_new_tokens)
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outputs += tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
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return [output.split("<|output|>")[1] for output in outputs]
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model_name = "numind/NuExtract-v1.5"
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device = "cuda"
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device).eval()
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
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superior performance and efficiency. Mistral 7B outperforms the best open 13B
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model (Llama 2) across all evaluated benchmarks, and the best released 34B
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model (Llama 1) in reasoning, mathematics, and code generation. Our model
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leverages grouped-query attention (GQA) for faster inference, coupled with sliding
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window attention (SWA) to effectively handle sequences of arbitrary length with a
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reduced inference cost. We also provide a model fine-tuned to follow instructions,
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Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
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automated benchmarks. Our models are released under the Apache 2.0 license.
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Code: <https://github.com/mistralai/mistral-src>
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Webpage: <https://mistral.ai/news/announcing-mistral-7b/>"""
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template = """{
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"Model": {
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"Name": "",
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"Number of parameters": "",
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"Number of max token": "",
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"Architecture": []
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||||
},
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||||
"Usage": {
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"Use case": [],
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"Licence": ""
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}
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}"""
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prediction = predict_NuExtract(model, tokenizer, [text], template)[0]
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print(prediction)
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```
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Sliding window prompting:
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```python
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import json
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MAX_INPUT_SIZE = 20_000
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MAX_NEW_TOKENS = 6000
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def clean_json_text(text):
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text = text.strip()
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text = text.replace("\#", "#").replace("\&", "&")
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return text
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def predict_chunk(text, template, current, model, tokenizer):
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current = clean_json_text(current)
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input_llm = f"<|input|>\n### Template:\n{template}\n### Current:\n{current}\n### Text:\n{text}\n\n<|output|>" + "{"
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input_ids = tokenizer(input_llm, return_tensors="pt", truncation=True, max_length=MAX_INPUT_SIZE).to("cuda")
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output = tokenizer.decode(model.generate(**input_ids, max_new_tokens=MAX_NEW_TOKENS)[0], skip_special_tokens=True)
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return clean_json_text(output.split("<|output|>")[1])
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def split_document(document, window_size, overlap):
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tokens = tokenizer.tokenize(document)
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print(f"\tLength of document: {len(tokens)} tokens")
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chunks = []
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if len(tokens) > window_size:
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for i in range(0, len(tokens), window_size-overlap):
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print(f"\t{i} to {i + len(tokens[i:i + window_size])}")
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chunk = tokenizer.convert_tokens_to_string(tokens[i:i + window_size])
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chunks.append(chunk)
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if i + len(tokens[i:i + window_size]) >= len(tokens):
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break
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else:
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chunks.append(document)
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print(f"\tSplit into {len(chunks)} chunks")
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return chunks
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def handle_broken_output(pred, prev):
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try:
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if all([(v in ["", []]) for v in json.loads(pred).values()]):
|
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# if empty json, return previous
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pred = prev
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except:
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# if broken json, return previous
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pred = prev
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return pred
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def sliding_window_prediction(text, template, model, tokenizer, window_size=4000, overlap=128):
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# split text into chunks of n tokens
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tokens = tokenizer.tokenize(text)
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chunks = split_document(text, window_size, overlap)
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# iterate over text chunks
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prev = template
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for i, chunk in enumerate(chunks):
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print(f"Processing chunk {i}...")
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pred = predict_chunk(chunk, template, prev, model, tokenizer)
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# handle broken output
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pred = handle_broken_output(pred, prev)
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# iterate
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prev = pred
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return pred
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```
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@ -0,0 +1,138 @@
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{
|
||||
"_name_or_path": "numind/NuExtract-v1.5",
|
||||
"architectures": [
|
||||
"Phi3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"auto_map": {
|
||||
"AutoConfig": "microsoft/Phi-3.5-mini-instruct--configuration_phi3.Phi3Config",
|
||||
"AutoModelForCausalLM": "microsoft/Phi-3.5-mini-instruct--modeling_phi3.Phi3ForCausalLM"
|
||||
},
|
||||
"bos_token_id": 1,
|
||||
"embd_pdrop": 0.0,
|
||||
"eos_token_id": 32000,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3072,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"max_position_embeddings": 131072,
|
||||
"model_type": "phi3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 32,
|
||||
"original_max_position_embeddings": 4096,
|
||||
"pad_token_id": 32000,
|
||||
"resid_pdrop": 0.0,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"long_factor": [
|
||||
1.0800000429153442,
|
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1.1100000143051147,
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1.1399999856948853,
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1.340000033378601,
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1.5899999141693115,
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1.600000023841858,
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1.6200000047683716,
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2.620000123977661,
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3.2300000190734863,
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3.2300000190734863,
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4.789999961853027,
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7.400000095367432,
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7.700000286102295,
|
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9.09000015258789,
|
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12.199999809265137,
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17.670000076293945,
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24.46000099182129,
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28.57000160217285,
|
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30.420001983642578,
|
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30.840002059936523,
|
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32.590003967285156,
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32.93000411987305,
|
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42.320003509521484,
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44.96000289916992,
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50.340003967285156,
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50.45000457763672,
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57.55000305175781,
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57.93000411987305,
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58.21000289916992,
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60.1400032043457,
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62.61000442504883,
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62.62000274658203,
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62.71000289916992,
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63.1400032043457,
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63.1400032043457,
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63.77000427246094,
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63.93000411987305,
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63.96000289916992,
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63.970001220703125,
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||||
64.02999877929688,
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||||
64.06999969482422,
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64.08000183105469,
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64.12000274658203,
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||||
64.41000366210938,
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64.4800033569336,
|
||||
64.51000213623047,
|
||||
64.52999877929688,
|
||||
64.83999633789062
|
||||
],
|
||||
"short_factor": [
|
||||
1.0,
|
||||
1.0199999809265137,
|
||||
1.0299999713897705,
|
||||
1.0299999713897705,
|
||||
1.0499999523162842,
|
||||
1.0499999523162842,
|
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1.0499999523162842,
|
||||
1.0499999523162842,
|
||||
1.0499999523162842,
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||||
1.0699999332427979,
|
||||
1.0999999046325684,
|
||||
1.1099998950958252,
|
||||
1.1599998474121094,
|
||||
1.1599998474121094,
|
||||
1.1699998378753662,
|
||||
1.2899998426437378,
|
||||
1.339999794960022,
|
||||
1.679999828338623,
|
||||
1.7899998426437378,
|
||||
1.8199998140335083,
|
||||
1.8499997854232788,
|
||||
1.8799997568130493,
|
||||
1.9099997282028198,
|
||||
1.9399996995925903,
|
||||
1.9899996519088745,
|
||||
2.0199997425079346,
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||||
2.0199997425079346,
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||||
2.0199997425079346,
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2.0199997425079346,
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2.0199997425079346,
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2.0199997425079346,
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2.0299997329711914,
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||||
2.0299997329711914,
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2.0299997329711914,
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2.0299997329711914,
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2.0299997329711914,
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2.0299997329711914,
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2.0299997329711914,
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2.0299997329711914,
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2.0299997329711914,
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2.0799996852874756,
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2.0899996757507324,
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2.189999580383301,
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2.2199995517730713,
|
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2.5899994373321533,
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2.729999542236328,
|
||||
2.749999523162842,
|
||||
2.8399994373321533
|
||||
],
|
||||
"type": "longrope"
|
||||
},
|
||||
"rope_theta": 10000.0,
|
||||
"sliding_window": 262144,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.44.0",
|
||||
"use_cache": true,
|
||||
"vocab_size": 32064
|
||||
}
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||||
|
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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{
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||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 32000,
|
||||
"pad_token_id": 32000,
|
||||
"transformers_version": "4.44.0"
|
||||
}
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|
|
@ -0,0 +1,37 @@
|
|||
from typing import Dict, List, Any
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
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||||
import torch
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||||
import os
|
||||
|
||||
|
||||
MAX_INPUT_SIZE = 10_000
|
||||
MAX_NEW_TOKENS = 4_000
|
||||
|
||||
def clean_json_text(text):
|
||||
"""
|
||||
Cleans JSON text by removing leading/trailing whitespace and escaping special characters.
|
||||
"""
|
||||
text = text.strip()
|
||||
text = text.replace("\#", "#").replace("\&", "&")
|
||||
return text
|
||||
|
||||
class EndpointHandler:
|
||||
def __init__(self, path=""):
|
||||
# load model and processor from path
|
||||
self.model = AutoModelForCausalLM.from_pretrained(path,
|
||||
trust_remote_code=True,
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto")
|
||||
self.model.eval()
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(path)
|
||||
|
||||
def __call__(self, data: Dict[str, Any]) -> str:
|
||||
data = data.pop("inputs")
|
||||
template = data.pop("template")
|
||||
text = data.pop("text")
|
||||
input_llm = f"<|input|>\n### Template:\n{template}\n### Text:\n{text}\n\n<|output|>" + "{"
|
||||
|
||||
input_ids = self.tokenizer(input_llm, return_tensors="pt", truncation=True, max_length=MAX_INPUT_SIZE).to("cuda")
|
||||
output = self.tokenizer.decode(self.model.generate(**input_ids, max_new_tokens=MAX_NEW_TOKENS)[0], skip_special_tokens=True)
|
||||
|
||||
return clean_json_text(output.split("<|output|>")[1])
|
||||
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@ -0,0 +1,202 @@
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|||
{
|
||||
"metadata": {
|
||||
"total_size": 7642159104
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00002-of-00002.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.gate_up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.qkv_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.gate_up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.qkv_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.gate_up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.qkv_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.mlp.gate_up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
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||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32002": {
|
||||
"content": "<|placeholder1|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32003": {
|
||||
"content": "<|placeholder2|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32004": {
|
||||
"content": "<|placeholder3|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32005": {
|
||||
"content": "<|placeholder4|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32006": {
|
||||
"content": "<|system|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32007": {
|
||||
"content": "<|end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32008": {
|
||||
"content": "<|placeholder5|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32009": {
|
||||
"content": "<|placeholder6|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32010": {
|
||||
"content": "<|user|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"legacy": false,
|
||||
"max_length": 4000,
|
||||
"model_max_length": 131072,
|
||||
"pad_to_multiple_of": null,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"pad_token_type_id": 0,
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"stride": 0,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"truncation_side": "right",
|
||||
"truncation_strategy": "longest_first",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Loading…
Reference in New Issue