Instantiate DatasetContextClassifier in DatasetParser and guard null case

Readme.md

@@ -33,15 +33,15 @@ The easiest way to deploy and run the service is to use the Docker image.
 It's possible to use a Docker image via [docker HUB](https://hub.docker.com/r/grobid/datastet), pull the image (5.25GB) as follows:
 
 ```bash
-docker pull grobid/datastet:0.8.0
+docker pull grobid/datastet:0.9.0
 ```
 
 (check the latest version on project's [docker HUB](https://hub.docker.com/r/grobid/datastet)!)
 
 After pulling or building the Docker image, you can now run the `datastet` service as a container:
 
 ```bash
->  docker run --rm --gpus all -it --init --ulimit core=0 -p 8060:8060 grobid/datastet:0.8.0
+>  docker run --rm --gpus all -it --init --ulimit core=0 -p 8060:8060 grobid/datastet:0.9.0
 ```
 
 The build image includes the automatic support of GPU when available on the host machine via the parameter `--gpus all` (with automatic recognition of the CUDA version), with fall back to CPU if GPU are not available. 
@@ -51,15 +51,15 @@ The `datastet` service is available at the default host/port `localhost:8060`, b
 launch time of the container as follow:
 
 ```bash
-> docker run --rm --gpus all -it --init --ulimit core=0 -p 8080:8060 grobid/datastet:0.8.0
+> docker run --rm --gpus all -it --init --ulimit core=0 -p 8080:8060 grobid/datastet:0.9.0
 ```
 
 By default, BidLSTM-CFR with ELMo model if used for the dataset mention recognition (it performs better than SciBERT with 3 points F1-score). 
 Every classification models are fine-tuned SciBERT models. 
 To modify the configuration without rebuilding the image - for instance rather use the SciBERT model, it is possible to mount a modified config file at launch as follows:
 
 ```bash
-> docker run --rm --gpus all -it --init --ulimit core=0 -p 8060:8060 -v /home/lopez/grobid/datastet/resources/config/config.yml:/opt/grobid/datastet/resources/config/config.yml:ro  grobid/datastet:0.8.0
+> docker run --rm --gpus all -it --init --ulimit core=0 -p 8060:8060 -v /home/lopez/grobid/datastet/resources/config/config.yml:/opt/grobid/datastet/resources/config/config.yml:ro  grobid/datastet:0.9.0
 ```
 
 As an alterntive, a docker image for the `datastet` service can be built with the project Dockerfile to match the
@@ -74,15 +74,15 @@ current master version. The complete process is as follows:
 - from the GROBID root installation (`grobid/`), launch the docker build:
 
 ```bash
-> docker build -t grobid/datastet:0.8.0 --build-arg GROBID_VERSION=0.8.0 --file Dockerfile.datastet .
+> docker build -t grobid/datastet:0.9.0 --build-arg GROBID_VERSION=0.9.0 --file Dockerfile.datastet .
 ```
 
 The Docker image build take several minutes, installing GROBID, datastet, a complete Python Deep Learning environment based on [DeLFT](https://github.com/kermitt2/delft) and deep learning models downloaded from the internet (one fine-tuned model with a BERT layer has a size of around 400 MB). 
 The resulting image is thus very large, more than 10GB, due to the deep learning resources and models.
 
 ## Build & Run
 
-Building the module requires JDK 1.8 or higher. First install and build the latest development version of GROBID (currently `0.8.2-SNAPSHOT`) as explained by the [documentation](http://grobid.readthedocs.org), together with [DeLFT](https://github.com/kermitt2/delft) (currently version `0.3.2`) for Deep Learning model support.
+Building the module requires JDK 1.8 or higher. First install and build the latest development version of GROBID (currently `0.9.0`) as explained by the [documentation](http://grobid.readthedocs.org), together with [DeLFT](https://github.com/kermitt2/delft) (currently version `0.3.2`) for Deep Learning model support.
 
 Under the installed and built `grobid/` directory, clone the present module `datastet` (it will appear as sibling sub-project to grobid-core, grobid-trainer, etc.):
 
@@ -201,7 +201,7 @@ The output data format is JSON format as follows:
 ```json 
 {
   "application" : "datastet",
-  "version" : "0.8.0",
+  "version" : "0.9.0",
   "date" : "2024-10-04T07:31+0000",
   "md5" : "F1F1BC4C79AC8A8E85D1EAF4265B97FB",
   "mentions" : [ {

build.gradle

@@ -31,7 +31,7 @@ apply plugin: 'base'
 apply plugin: 'com.github.kt3k.coveralls'
 
 group = "org.grobid.datastet"
-version = '0.8.2-SNAPSHOT'
+version = '0.9.0'
 
 description = """datastet"""
 

resources/config/config-docker.yml

@@ -1,4 +1,4 @@
-version: "0.8.2"
+version: "0.9.0"
 
 corpusPath: "./resources/dataset/dataseer/corpus"
 templatePath: "./resources/dataset/dataseer/crfpp-templates/dataseer.template"

resources/config/config.yml

@@ -1,4 +1,4 @@
-version: "0.8.2"
+version: "0.9.0"
 
 corpusPath: "./resources/dataset/dataseer/corpus"
 templatePath: "./resources/dataset/dataseer/crfpp-templates/dataseer.template"

src/main/java/org/grobid/core/engines/DatasetParser.java

@@ -83,6 +83,10 @@ public class DatasetParser extends AbstractParser {
     private static final java.util.concurrent.atomic.AtomicBoolean disambiguatorWarningLogged =
             new java.util.concurrent.atomic.AtomicBoolean(false);
 
+    // guard to warn only once about the context classifier not being available
+    private static final java.util.concurrent.atomic.AtomicBoolean contextClassifierWarningLogged =
+            new java.util.concurrent.atomic.AtomicBoolean(false);
+
     private EngineParsers parsers;
     private DatastetServiceConfiguration datastetConfiguration;
     private DataTypeClassifier dataTypeClassifier;
@@ -101,6 +105,12 @@ private static void warnDisambiguatorNotAvailableOnce() {
         }
     }
 
+    private static void warnContextClassifierNotAvailableOnce() {
+        if (contextClassifierWarningLogged.compareAndSet(false, true)) {
+            LOGGER.warn("Dataset context classifier is not available, context classification will be skipped");
+        }
+    }
+
     public static DatasetParser getInstance(
             DatastetServiceConfiguration configuration,
             DataTypeClassifier dataTypeClassifier,
@@ -128,12 +138,16 @@ private DatasetParser(DatastetServiceConfiguration configuration) {
                 GrobidCRFEngine.valueOf(configuration.getDatastetConfiguration().getModel("datasets").engine.toUpperCase()),
                 configuration.getDatastetConfiguration().getModel("datasets").delft.architecture);
 
-        this.dataTypeClassifier = dataTypeClassifier;
         DatastetLexicon.getInstance();
         this.parsers = new EngineParsers();
         this.datastetConfiguration = configuration;
         this.disambiguator = DatasetDisambiguator.getInstance(configuration.getDatastetConfiguration());
-        this.datasetContextClassifier = datasetContextClassifier;
+        try {
+            this.datasetContextClassifier = DatasetContextClassifier.getInstance(configuration);
+        } catch (Exception e) {
+            LOGGER.warn("Dataset context classifier is not available, context classification will be skipped", e);
+            this.datasetContextClassifier = null;
+        }
     }
 
     public List<List<Dataset>> processing(List<DatasetDocumentSequence> tokensList) {
@@ -1482,7 +1496,11 @@ public Pair<List<List<Dataset>>, Document> processPDF(File file,
                 entities = markDAS(entities, availabilityTokens);
 
             // finally classify the context for predicting the role of the dataset mention
-            entities = this.datasetContextClassifier.classifyDocumentContexts(entities);
+            if (this.datasetContextClassifier != null) {
+                entities = this.datasetContextClassifier.classifyDocumentContexts(entities);
+            } else {
+                warnContextClassifierNotAvailableOnce();
+            }
 
         } catch (Exception e) {
             //e.printStackTrace();