NebuIA
/

nebuia_extract_small

@@ -49,10 +49,9 @@ language:
 - es
 ---
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 ## Model Details
@@ -61,185 +60,52 @@ language:
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 - es
 ---
+# Structure Extraction Model
+nebuia_extract_small is an extraction model inspired by NuExtract. LegalExtract-ES 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
 <!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [NebuIA](https://nebuia.com)
+- **Language(s) (NLP):** es
+- **License:** mit
+- **Finetuned from model [optional]:** [Qween2 1.5b](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct)
 ## 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)
+```