numind
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NuExtract-v1.5

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 ---
+license: mit
+language:
+- multilingual
+tags:
+- nlp
+base_model: microsoft/Phi-3.5-mini-instruct
+---
+# NuExtract-v1.5 by NuMind 🔥
+NuExtract 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).
+To use the model, provide an input text and a JSON template describing the information you need to extract.
+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.
+Try it here: <space-link>
+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)
+**Checkout other models by NuMind:**
+- NuExtract Version 1 Models: [0.5B](https://huggingface.co/numind/NuExtract-tiny), [3.8B](https://huggingface.co/numind/NuExtract), [7B](https://huggingface.co/numind/NuExtract-large)
+- SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
+- SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
+- SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
+## Benchmark
+Include result plots.
+## Usage
+To use the model:
+```python
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+def predict_NuExtract(model, tokenizer, texts, template, batch_size=1, max_length=10_000, max_new_tokens=4_000):
+    template = json.dumps(json.loads(template), indent=4)
+    prompts = [f"""<|input|>\n### Template:\n{template}\n### Text:\n{text}\n\n<|output|>""" for text in texts]
+    outputs = []
+    with torch.no_grad():
+		    for i in range(0, len(prompts), batch_size):
+				    batch_prompts = prompts[i:i+batch_size]
+				    batch_encodings = tokenizer(batch_prompts, return_tensors="pt", truncation=True, padding=True, max_length=max_length).to(model.device)
+				    pred_ids = model.generate(**batch_encodings, max_new_tokens=max_new_tokens)
+				    outputs += tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+    return [output.split("<|output|>")[1] for output in outputs]
+model_name = "numind/NuExtract-v1.5"
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device).eval()
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+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/>"""
+template = """{
+    "Model": {
+        "Name": "",
+        "Number of parameters": "",
+        "Number of max token": "",
+        "Architecture": []
+    },
+    "Usage": {
+        "Use case": [],
+        "Licence": ""
+    }
+}"""
+prediction = predict_NuExtract(model, tokenizer, [text], template)[0]
+print(prediction)
+```