Inference

Authors

Tong Zhu

Last Update

May 26th, 2022

DocEE provides convenient inference APIs for generating submitting results (like DuEE-fin) or make predictions with plain texts as input.

Here, we introduce the inference usages.

Generating Results to Submit Online Platforms

Since the DuEE-fin dataset needs to be evaluated on the online test set, a submit file must be generated offline in advance.

To make inference results on DuEE-fin, make sure you have set the correct run_mode in settings. If you use our provided scripts, this should not be a problem. Please be aware that run_mode is different from templates. It is used for identifying file names for different data.

There are some arguments that relates to inference: - run_inference: whether to make inference - load_inference: whether to load inference dataset - inference_epoch: which epoch is selected to load model parameters. -1 to use the best model. - inference_dump_filepath: the result file path - filtered_data_types: unk must be included in it to load inference dataset. i.e. o2o,o2m,m2m,unk

For example, you may want to make inference like:

CUDA_VISIBLE_DEVICES=0 python -u run_dee_task.py \
    --data_dir='Data/DuEEData' \
    --task_name='dueefin_w_tgg' \
    --model_type=${MODEL_NAME} \
    --cpt_file_name=${MODEL_NAME} \
    --eval_batch_size=16 \
    --run_mode='dueefin_w_tgg' \
    --filtered_data_types='o2o,o2m,m2m,unk' \
    --skip_train=True \
    --load_dev=False \
    --load_test=False \
    --load_inference=True \
    --inference_epoch=-1 \
    --run_inference=True \
    --inference_dump_filepath='dueefin_t1_submit_new.json' \
    --add_greedy_dec=False

After obtaining the result file dueefin_t1_submit_new.json, you may want to remove redundant information via the dueefin_post_process.py script. Set to_remove_filepath and save_filepath items in the script, and run the following command in shell to get the final file.

python dueefin_post_process.py

Make Predictions from Plain Texts

Yes! DocEE provides a simple API to predict event instances.

Basically, you need to load a trained model parameter at the beginning, for example, dee_task.resume_cpt_at(epoch_idx). Then you could use dee_task.predict_one(doc_string) to obtain the predicted event instances.

Check inference.py for the detailed demo usage.