Classification

At a glance

The classification task encapsulates algorithms for document classification: classifying the input text into one or more of a pre-determined set of labels.

The task offers implementations of strong classification algorithms from three different families: classic ML, deep-learning and transformers. It supports multi-label and multi-class problems and its special cases: single-label, multi-class tasks and, respectively, binary classification tasks.

BERT is a transformer-based architecture, built for multi-class and multi-label text classification on short texts. Utilizes Multilingual BERT pretrained models.

Transformer is a transformer-based architecture, built for multi-class and multi-label text classification on short texts. Utilizes BERT and RoBERTa pretrained models.

Ensemble is a weighted ensemble of SVM and CNN algorithms; it computes the weighted mean of a set of classification predictions using confidence scores. SVM is a support vector machine classifier, which may be trained using any type of input embedding / vectorization task's predictions as feature vectors, e.g., USE embeddings or TF-IDF vectorizers. SVM supports multi-class and multi-label text classification and produces confidence scores via Platt Scaling. CNN is a simple convolutional network architecture, built for multi-class and multi-label text classification on short texts. CNN utilizes GloVe embeddings.

Pretrained models

Model names are listed below. For language support, see Supported languages.

The models have been tested on data from news reports and general web pages.

Running models

The Classification model request accepts the following fields:

Example requests

curl -s \
  "http://localhost:8080/v1/watson.runtime.nlp.v1/NlpService/ClassificationPredict" \
  -H "accept: application/json" \
  -H "content-type: application/json" \
  -H "Grpc-Metadata-mm-model-id: classification_ensemble-workflow_lang_en_tone-stock" \
  -d '{ "raw_document": { "text": "I hate school. School is bad." } }'

{"classes":[
  {"className":"frustrated", "confidence":0.74309075},
  {"className":"sad", "confidence":0.20021306},
  {"className":"impolite", "confidence":0.07343281},
  {"className":"excited", "confidence":0.029446114},
  {"className":"sympathetic", "confidence":0.02796789},
  {"className":"polite", "confidence":0.016257437},
  {"className":"satisfied", "confidence":0.01131451}],
 "producerId":{
  "name":"Voting based Ensemble",
  "version":"0.0.1"
  }
}

import grpc

from watson_nlp_runtime_client import (
    common_service_pb2,
    common_service_pb2_grpc,
    syntax_types_pb2,
)

channel = grpc.insecure_channel("localhost:8085")

stub = common_service_pb2_grpc.NlpServiceStub(channel)

request = common_service_pb2.ClassificationRequest(
    raw_document=syntax_types_pb2.RawDocument(text="I hate school. School is bad."),
)

  response = stub.ClassificationPredict(
    request, metadata=[("mm-model-id", "classification_ensemble-workflow_lang_en_tone-stock")]
)

print(response)

classes {
  class_name: "frustrated"
  confidence: 0.743090749
}
classes {
  class_name: "sad"
  confidence: 0.20021306
}
classes {
  class_name: "impolite"
  confidence: 0.0734328106
}
classes {
  class_name: "excited"
  confidence: 0.0294461139
}
classes {
  class_name: "sympathetic"
  confidence: 0.0279678907
}
classes {
  class_name: "polite"
  confidence: 0.0162574369
}
classes {
  class_name: "satisfied"
  confidence: 0.0113145104
}
producer_id {
  name: "Voting based Ensemble"
  version: "0.0.1"
}