Update README.md

README.md

@@ -5,9 +5,11 @@ language:
 ---
 # Structure Extraction Model by NuMind 🔥
 
-NuExtract_tiny is a fine-tuned version of [Qwen1.5-0.5](https://huggingface.co/Qwen/Qwen1.5-0.5B),on a private high-quality syntactic 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. 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.
+NuExtract_tiny is a version of [Qwen1.5-0.5](https://huggingface.co/Qwen/Qwen1.5-0.5B), fine-tuned on a private high-quality synthetic dataset for information extraction. To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract.
 
-Note: This model while providing good performance in 0 shot, is intendeed to be fine-tune on a specific task (at least 30 example needed)
+Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.
+
+Note: While this model provides good 0 shot performance, it is intendeed to be fine-tune on a specific task (>=30 examples).
 
 We also provide a base (3.8B) and large(7B) version of this model: [NuExtract](https://huggingface.co/numind/NuExtract) and [NuExtract-large](https://huggingface.co/numind/NuExtract-large)
 
@@ -26,7 +28,7 @@ import json
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 
-def predict_NuExtract(model,tokenizer,text, schema,example = ["","",""]):
+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:
@@ -34,7 +36,7 @@ def predict_NuExtract(model,tokenizer,text, schema,example = ["","",""]):
           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")
+    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]
@@ -72,7 +74,7 @@ schema = """{
     }
 }"""
 
-prediction = predict_NuExtract(model,tokenizer,text, schema,example = ["","",""])
+prediction = predict_NuExtract(model, tokenizer, text, schema, example=["","",""])
 print(prediction)
 
 ```