Awesome

Evaluating-filtering-coling24

Code and prompt templates for evaluation-filtering

• Some data were not uploaded due to size restrictions, but you can find all the datasets covered in this paper by the references in the paper.

You can run the main_framework.py file with additional arguements：

• use --dname datasetname to specify the dataset for training and testing, and this will also create a new directory under your current path. The directory name is the name of the dataset, e.g. HacRED.
• use --do_train to train an entity-extraction model for the dataset.
• use --cons_candidate to cauculate the candidate entity pairs and output a candidate file in your datasetname directory. It is recommended to set the --filter parameter to True when generating candidates, that is, use the evaluation model proposed in our papaer to extract generate high-precision candidate pairs.
• use --do_eval to calculate the metrics (precision, recall and F1 score) for the results. Please specify the file path of candidates and results in the line 319-324 of main_framework.py. The results files are generated by run the llm_batch_inference.py file.

You can run the relcombtrainer.py file to train and save our evaluation model:

• use --dname datasetname to specify the dataset for training, and the evaluation model will be saved in the datasetname directory.

Our instructions and prompts are in the llm_batch_inference.py file, and you can run this file to perform our evaulation-filtering LLM-based extraction framework:

• use --dname datasetname to specify the dataset for testing. Please specify the file path of candidates and save path of results in the line 168-190 of this code file.
• use --model to specify the LLM model name. Please modify the model path in the line 142-144 of this code file.
• use --peft to specify whether use peft model or not.