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version: "0.8.0"
corpusPath: "./resources/dataset/dataseer/corpus"
templatePath: "./resources/dataset/dataseer/crfpp-templates/dataseer.template"
grobidHome: "/opt/grobid/grobid-home"
tmpPath: "/opt/grobid/grobid-home/tmp/"
# path to Pub2TEI repository as available at https://github.com/kermitt2/Pub2TEI
pub2teiPath: "/opt/Pub2TEI/"
gluttonHost: "https://cloud.science-miner.com/glutton"
gluttonPort:
# entity-fishing server information for performing entity disambiguation
# for https, indicate 443 as port
entityFishingHost: cloud.science-miner.com/nerd
entityFishingPort: 443
#entityFishingHost: localhost
#entityFishingPort: 8090
# if true we use binary classifiers for the contexts, otherwise use a single multi-label classifier
# binary classifiers perform better, but havier to use
useBinaryContextClassifiers: false
# sequence labeling model (identify data-related sections)
models:
# model for zones
- name: "dataseer"
engine: "wapiti"
#engine: "delft"
wapiti:
# wapiti training parameters, they will be used at training time only
epsilon: 0.00001
window: 20
nbMaxIterations: 2000
# classifier model, dataset binary (datset or not dataset in the current sentence)
- name: "dataseer-binary"
engine: "delft"
delft:
# deep learning parameters
#architecture: "gru"
architecture: "bert"
#embeddings_name: "word2vec"
transformer: "allenai/scibert_scivocab_cased"
# identification of the data type (first level hierarchy)
- name: "dataseer-first"
engine: "delft"
delft:
# deep learning parameters
#architecture: "gru"
architecture: "bert"
#embeddings_name: "word2vec"
transformer: "allenai/scibert_scivocab_cased"
# mention context classification (reuse binary for the moment)
- name: "dataseer-reuse"
engine: "delft"
delft:
# deep learning parameters
#architecture: "gru"
architecture: "bert"
#embeddings_name: "word2vec"
transformer: "allenai/scibert_scivocab_cased"
# model for dataset mention recognition
- name: "datasets"
#engine: "wapiti"
engine: "delft"
wapiti:
# wapiti training parameters, they will be used at training time only
epsilon: 0.00001
window: 20
nbMaxIterations: 2000
delft:
# deep learning parameters
#architecture: "BidLSTM_CRF"
architecture: "BERT_CRF"
#transformer: "allenai/scibert_scivocab_cased"
transformer: "michiyasunaga/LinkBERT-basecased"
#useELMo: true
#embeddings_name: "glove-840B"
runtime:
# parameters used at runtime/prediction
max_sequence_length: 200
#max_sequence_length: 300
batch_size: 20
- name: "context"
engine: "delft"
delft:
#architecture: "gru"
#embeddings_name: "glove-840B"
architecture: "bert"
transformer: "michiyasunaga/LinkBERT-basecased"
- name: "context_used"
engine: "delft"
delft:
#architecture: "gru"
#embeddings_name: "glove-840B"
architecture: "bert"
transformer: "michiyasunaga/LinkBERT-basecased"
- name: "context_creation"
engine: "delft"
delft:
#architecture: "gru"
#embeddings_name: "glove-840B"
architecture: "bert"
transformer: "michiyasunaga/LinkBERT-basecased"
- name: "context_shared"
engine: "delft"
delft:
#architecture: "gru"
#embeddings_name: "glove-840B"
architecture: "bert"
transformer: "michiyasunaga/LinkBERT-basecased"
# Limit the maximum number of requests (0, no limit)
maxParallelRequests: 0
# CORS configuration for the web API service
corsAllowedOrigins: "*"
corsAllowedMethods: "OPTIONS,GET,PUT,POST,DELETE,HEAD"
corsAllowedHeaders: "X-Requested-With,Content-Type,Accept,Origin"
server:
type: custom
idleTimeout: 120 seconds
applicationConnectors:
- type: http
port: 8060
adminConnectors:
- type: http
port: 8061
registerDefaultExceptionMappers: false
maxThreads: 2048
maxQueuedRequests: 2048
acceptQueueSize: 2048
requestLog:
appenders: []
# these logging settings apply to the service usage mode
logging:
level: INFO
loggers:
org.apache.pdfbox.pdmodel.font.PDSimpleFont: "OFF"
org.glassfish.jersey.internal: "OFF"
com.squarespace.jersey2.guice.JerseyGuiceUtils: "OFF"
appenders:
- type: console
threshold: INFO
timeZone: UTC
# uncomment to have the logs in json format
#layout:
# type: json |