token-classification lsr

Relation Extraction

You can test the model at SGNLP-Demo.<br /> If you want to find out more information, please contact us at sg-nlp@aisingapore.org.

Table of Contents

Model Details

Model Name: LSR

How to Get Started With the Model

Install Python package

SGnlp is an initiative by AI Singapore's NLP Hub. They aim to bridge the gap between research and industry, promote translational research, and encourage adoption of NLP techniques in the industry. <br><br> Various NLP models, other than relation extraction are available in the python package. You can try them out at SGNLP-Demo | SGNLP-Github.

pip install sgnlp

Examples

For more full code (such as Relation-Extraction), please refer to this github. <br> Alternatively, you can also try out the SGNLP-Demo | SGNLP-Docs for Relation extraction using LSR model.

Example of Relation Extraction (using LSR model):

from sgnlp.models.lsr import LsrModel, LsrConfig, LsrPreprocessor, LsrPostprocessor
from transformers import cached_path

# Download files from azure blob storage
rel2id_path = cached_path('https://storage.googleapis.com/sgnlp-models/models/lsr/rel2id.json')
word2id_path = cached_path('https://storage.googleapis.com/sgnlp-models/models/lsr/word2id.json')
ner2id_path = cached_path('https://storage.googleapis.com/sgnlp-models/models/lsr/ner2id.json')
rel_info_path = cached_path('https://storage.googleapis.com/sgnlp-models/models/lsr/rel_info.json')

PRED_THRESHOLD = 0.3
preprocessor = LsrPreprocessor(rel2id_path=rel2id_path, word2id_path=word2id_path, ner2id_path=ner2id_path)
postprocessor = LsrPostprocessor.from_file_paths(rel2id_path=rel2id_path, rel_info_path=rel_info_path,
                                                 pred_threshold=PRED_THRESHOLD)

# Load model
config = LsrConfig.from_pretrained('https://storage.googleapis.com/sgnlp-models/models/lsr/v2/config.json')
model = LsrModel.from_pretrained('https://storage.googleapis.com/sgnlp-models/models/lsr/v2/pytorch_model.bin', config=config)
model.eval()

# DocRED-like instance
instance = {
    "vertexSet": [[{"name": "Lark Force", "pos": [0, 2], "sent_id": 0, "type": "ORG"},
                   {"sent_id": 3, "type": "ORG", "pos": [2, 4], "name": "Lark Force"},
                   {"name": "Lark Force", "pos": [3, 5], "sent_id": 4, "type": "ORG"}],
                  [{"name": "Australian Army", "pos": [4, 6], "sent_id": 0, "type": "ORG"}],
                  [{"pos": [9, 11], "type": "TIME", "sent_id": 0, "name": "March 1941"}],
                  [{"name": "World War II", "pos": [12, 15], "sent_id": 0, "type": "MISC"}],
                  [{"name": "New Britain", "pos": [18, 20], "sent_id": 0, "type": "LOC"}],
                  [{"name": "New Ireland", "pos": [21, 23], "sent_id": 0, "type": "LOC"}],
                  [{"name": "John Scanlan", "pos": [6, 8], "sent_id": 1, "type": "PER"}],
                  [{"name": "Australia", "pos": [13, 14], "sent_id": 1, "type": "LOC"}],
                  [{"name": "Rabaul", "pos": [17, 18], "sent_id": 1, "type": "LOC"},
                   {"name": "Rabaul", "pos": [12, 13], "sent_id": 3, "type": "LOC"}],
                  [{"name": "Kavieng", "pos": [19, 20], "sent_id": 1, "type": "LOC"},
                   {"name": "Kavieng", "pos": [14, 15], "sent_id": 3, "type": "LOC"}],
                  [{"pos": [22, 24], "type": "MISC", "sent_id": 1, "name": "SS Katoomba"}],
                  [{"pos": [25, 27], "type": "MISC", "sent_id": 1, "name": "MV Neptuna"}],
                  [{"name": "HMAT Zealandia", "pos": [28, 30], "sent_id": 1, "type": "MISC"}],
                  [{"name": "Imperial Japanese Army", "pos": [8, 11], "sent_id": 3, "type": "ORG"}],
                  [{"pos": [18, 20], "type": "TIME", "sent_id": 3, "name": "January 1942"}],
                  [{"name": "Japan", "pos": [8, 9], "sent_id": 4, "type": "LOC"}],
                  [{"pos": [12, 13], "type": "MISC", "sent_id": 4, "name": "NCOs"}],
                  [{"name": "USS Sturgeon", "pos": [20, 22], "sent_id": 4, "type": "MISC"}],
                  [{"sent_id": 4, "type": "MISC", "pos": [27, 29], "name": "Montevideo Maru"}],
                  [{"name": "Japanese", "pos": [5, 6], "sent_id": 5, "type": "LOC"}],
                  [{"pos": [15, 16], "type": "NUM", "sent_id": 5, "name": "1,050"}],
                  [{"pos": [17, 18], "type": "NUM", "sent_id": 5, "name": "1,053"}]],
    "labels": [
        {"r": "P607", "h": 1, "t": 3, "evidence": [0]},
        {"r": "P17", "h": 1, "t": 7, "evidence": [0, 1]},
        {"r": "P241", "h": 6, "t": 1, "evidence": [0, 1]},
        {"r": "P607", "h": 6, "t": 3, "evidence": [0, 1]},
        {"r": "P27", "h": 6, "t": 7, "evidence": [0, 1]},
        {"r": "P1344", "h": 7, "t": 3, "evidence": [0, 1]},
        {"r": "P607", "h": 13, "t": 3, "evidence": [0, 3]},
        {"r": "P17", "h": 13, "t": 15, "evidence": [3, 4, 5]},
        {"r": "P17", "h": 13, "t": 19, "evidence": [3, 4, 5]},
        {"r": "P1344", "h": 15, "t": 3, "evidence": [0, 3, 4, 5]},
        {"r": "P172", "h": 15, "t": 19, "evidence": [4, 5]},
        {"r": "P607", "h": 17, "t": 3, "evidence": [0, 4]},
        {"r": "P17", "h": 11, "t": 7, "evidence": [1]},
        {"r": "P17", "h": 12, "t": 7, "evidence": [0, 1]},
        {"r": "P137", "h": 0, "t": 1, "evidence": [0, 1]},
        {"r": "P571", "h": 0, "t": 2, "evidence": [0]},
        {"r": "P607", "h": 0, "t": 3, "evidence": [0]},
        {"r": "P17", "h": 0, "t": 7, "evidence": [0, 1]}],
    "title": "Lark Force",
    "sents": [
        ["Lark", "Force", "was", "an", "Australian", "Army", "formation", "established", "in", "March", "1941",
         "during", "World", "War", "II", "for", "service", "in", "New", "Britain", "and", "New", "Ireland", "."],
        ["Under", "the", "command", "of", "Lieutenant", "Colonel", "John", "Scanlan", ",", "it", "was", "raised", "in",
         "Australia", "and", "deployed", "to", "Rabaul", "and", "Kavieng", ",", "aboard", "SS", "Katoomba", ",", "MV",
         "Neptuna", "and", "HMAT", "Zealandia", ",", "to", "defend", "their", "strategically", "important", "harbours",
         "and", "airfields", "."],
        ["The", "objective", "of", "the", "force", ",", "was", "to", "maintain", "a", "forward", "air", "observation",
         "line", "as", "long", "as", "possible", "and", "to", "make", "the", "enemy", "fight", "for", "this", "line",
         "rather", "than", "abandon", "it", "at", "the", "first", "threat", "as", "the", "force", "was", "considered",
         "too", "small", "to", "withstand", "any", "invasion", "."],
        ["Most", "of", "Lark", "Force", "was", "captured", "by", "the", "Imperial", "Japanese", "Army", "after",
         "Rabaul", "and", "Kavieng", "were", "captured", "in", "January", "1942", "."],
        ["The", "officers", "of", "Lark", "Force", "were", "transported", "to", "Japan", ",", "however", "the", "NCOs",
         "and", "men", "were", "unfortunately", "torpedoed", "by", "the", "USS", "Sturgeon", "while", "being",
         "transported", "aboard", "the", "Montevideo", "Maru", "."],
        ["Only", "a", "handful", "of", "the", "Japanese", "crew", "were", "rescued", ",", "with", "none", "of", "the",
         "between", "1,050", "and", "1,053", "prisoners", "aboard", "surviving", "as", "they", "were", "still",
         "locked", "below", "deck", "."]
    ]
}

tensor_doc = preprocessor([instance])
output = model(**tensor_doc)

result = postprocessor(output.prediction, [instance])

Training

The training datasets can be retrieved from Permuted dataset derived from Linguistic Data Consortium's (LDC) Wall Street Journal (WSJ) dataset. Please contact the authors to get the dataset if you have a valid LDC license.

Training Results

Model Parameters

Other Information