Model Specification

Meta Generation

Generate model meta.

Usage:

usage: meta.py [-h] -c CORPUS -m MODEL [--emb EMB] [-k]

Generate CosmEL meta data.

optional arguments:
  -h, --help            show this help message and exit
  -c CORPUS, --corpus CORPUS
                        store corpus data in directory "<CORPUS>/"
  -m MODEL, --model MODEL
                        store model data in directory "<MODEL>/"
  --emb EMB             embedding path; default is
                        "<CORPUS>/embeddings/purged_article.dim300.emb.bin"
  -k, --check           check arguments

Model Training

Training model.

Usage:

usage: train.py [-h] -c CORPUS -m MODEL [-i INPUT] -l {gid,nid,rid} -s
                STRUCTURE [-w WEIGHT] [-p PRETRAIN] [--epoch EPOCH]
                [--test_size TEST_SIZE] [-k]

Train CosmEL model.

optional arguments:
  -h, --help            show this help message and exit
  -c CORPUS, --corpus CORPUS
                        store corpus data in directory "<CORPUS>/"
  -m MODEL, --model MODEL
                        store model data in directory "<MODEL>/"
  -i INPUT, --input INPUT
                        load mention from "<CORPUS>/mention/<IN-DIR>/";
                        default is "purged_article_grid"
  -l {gid,nid,rid}, --label {gid,nid,rid}
                        training label type
  -s STRUCTURE, --structure STRUCTURE
                        model structure
  -w WEIGHT, --weight WEIGHT
                        output weight name; output model weight into
                        "<MODEL>/<WEIGHT>.<STRUCTURE>.weight.pt"; default
                        "[<PRETRAIN>+]<LABEL>"
  -p PRETRAIN, --pretrain PRETRAIN
                        pretrained weight name; load model weight from
                        "<MODEL>/<PRETRAIN>.<STRUCTURE>.weight.pt"
  --epoch EPOCH         train <EPOCH> times; default is 10
  --test_size TEST_SIZE
                        split <TEST-SIZE> mentions for testing; default is 0.3
  -k, --check           Check arguments

Model Predicting

Predicting label using model.

Usage:

usage: predict.py [-h] -c CORPUS -m MODEL [-i INPUT] [-o OUTPUT]
                  [-s STRUCTURE_EEM] [-S STRUCTURE_MTC] [-l LABEL_EEM]
                  [-L LABEL_MTC] [-k]

Apply CosmEL model.

optional arguments:
  -h, --help            show this help message and exit
  -c CORPUS, --corpus CORPUS
                        store corpus data in directory "<CORPUS>/"
  -m MODEL, --model MODEL
                        store model data in directory "<MODEL>/"; default is
                        "<CORPUS>/model/"
  -i INPUT, --input INPUT
                        load mention from "<CORPUS>/mention/<INPUT>/"; default
                        is "purged_article_rid"
  -o OUTPUT, --output OUTPUT
                        save mention into "<CORPUS>/mention/<OUTPUT>/";
                        default is "purged_article_nrid"
  -s STRUCTURE_EEM, --structure-eem STRUCTURE_EEM
                        use model structure <STRUCTURE-EEM> for entity
                        embeddings model
  -S STRUCTURE_MTC, --structure-mtc STRUCTURE_MTC
                        use model structure <STRUCTURE-MTC> for mention type
                        classifier
  -l LABEL_EEM, --label-eem LABEL_EEM
                        use label type <LABEL-EEM> for entity embeddings model
  -L LABEL_MTC, --label-mtc LABEL_MTC
                        use label type <LABEL-MTC> for mention type classifier
  -k, --check           Check arguments