Welcome to the exciting world of Information Extraction (IE)! In this article, we’ll walk you through the process of using the IEPile dataset, the latest breakthrough in schema-based information extraction. The IEPile dataset provides a comprehensive foundation for various information extraction tasks across multiple domains, primarily in English and Chinese.
What is IEPile?
The IEPile dataset is a meticulously compiled collection that integrates 26 English and 7 Chinese Information Extraction datasets, leading to a total of approximately 0.32 billion tokens. The dataset covers diverse domains including general knowledge, medical information, financial data, and more. It is designed to enhance the capabilities of AI models in extracting relevant information effectively and efficiently.
Downloading the Dataset
To get started, you’ll need to download the IEPile dataset. Here’s where you can find it:
Understanding the Structure of IEPile
The IEPile dataset employs a JSON-like format, comprising three main components: instruction, schema, and input. Let’s break this down with an analogy:
Think of the dataset as a recipe book. The instruction is like the cooking instructions, telling you what to do. The schema represents the ingredients you need, defining what types of information to extract (like specifying ‘vegetables’, ‘spices’, etc.). Finally, the input is the actual mixture you have in your pot (the text you want to analyze). When you follow these instructions correctly, you’ll end up with a perfectly cooked dish—just like extracting accurate information!
Setting Up the Environment
Before diving into using the models fine-tuned on IEPile, you need to set up your environment. Here’s how you can do it:
python
import torch
from transformers import (
AutoConfig,
AutoTokenizer,
AutoModelForCausalLM,
GenerationConfig
)
from peft import PeftModel
Loading and Using the Model
Once your environment is ready, you can load the fine-tuned model. Below is a guide on how to do this:
python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "baichuan-inc/Baichuan2-13B-Chat"
lora_path = "zjunlp/baichuan2-13b-iepile-lora"
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_path,
config=config,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, lora_path)
model.eval()
Running Inference
After loading the model, you can run inference for information extraction tasks. Here’s an example that showcases how to execute this using sample input:
python
input = "284 Robert Allenby (Australia) 69 71 71 73, Miguel Angel Martin (Spain) 75 70 71 68 (Allenby won at first play-off hole)"
sintstruct = f'''
{{"instruction": "You are an expert in named entity recognition. Please extract entities that match the schema definition from the input.",
"schema": ["person", "organization", "else", "location"],
"input": "{input}" }}
'''
input_ids = tokenizer.encode(sintstruct, return_tensors="pt").to(device)
generation_output = model.generate(
input_ids=input_ids,
generation_config=GenerationConfig(max_length=512, max_new_tokens=256, return_dict_in_generate=True)
)
output = tokenizer.decode(generation_output[0], skip_special_tokens=True)
print(output)
Troubleshooting
If you run into issues while setting up or using IEPile, consider the following troubleshooting tips:
- Ensure your environment has all necessary dependencies installed, particularly `torch` and `transformers`.
- Check GPU availability; if memory limits are reached, experiment with quantization to ease resource consumption.
- Review your input format to confirm it adheres to the required JSON structure.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.