sentiment analysis using DJL / published by https://github.com/dacr/code-examples-manager #65caa624-232d-483d-83d2-0d4ea0067540/99eda85629f074ad954edb56d281dded3cf70db3

djl-hugging-face-sentence-positiveness.sc

// summary : sentiment analysis using DJL
// keywords : djl, machine-learning, tutorial, sentiment, ai, @testable
// publish : gist
// authors : David Crosson
// license : Apache NON-AI License Version 2.0 (https://raw.githubusercontent.com/non-ai-licenses/non-ai-licenses/main/NON-AI-APACHE2)
// id : 65caa624-232d-483d-83d2-0d4ea0067540
// created-on : 2024-01-28T16:16:23+01:00
// managed-by : https://github.com/dacr/code-examples-manager
// run-with : scala-cli $file

// ---------------------
//> using scala  "3.3.1"
//> using dep "org.slf4j:slf4j-api:2.0.11"
//> using dep "org.slf4j:slf4j-simple:2.0.11"
//> using dep "net.java.dev.jna:jna:5.14.0"
//> using dep "ai.djl:api:0.26.0"
//> using dep "ai.djl:basicdataset:0.26.0"
//> using dep "ai.djl:model-zoo:0.26.0"
//> using dep "ai.djl.huggingface:tokenizers:0.26.0"
//> using dep "ai.djl.mxnet:mxnet-engine:0.26.0"
//> using dep "ai.djl.mxnet:mxnet-model-zoo:0.26.0"
//> using dep "ai.djl.pytorch:pytorch-engine:0.26.0"
//> using dep "ai.djl.pytorch:pytorch-model-zoo:0.26.0"
//> using dep "ai.djl.tensorflow:tensorflow-engine:0.26.0"
//> using dep "ai.djl.tensorflow:tensorflow-model-zoo:0.26.0"
//> using dep "ai.djl.paddlepaddle:paddlepaddle-engine:0.26.0"
//> using dep "ai.djl.paddlepaddle:paddlepaddle-model-zoo:0.26.0"
//> using dep "ai.djl.onnxruntime:onnxruntime-engine:0.26.0"
// ---------------------

System.setProperty("org.slf4j.simpleLogger.defaultLogLevel", "error")

import ai.djl.Application
import ai.djl.engine.Engine
import ai.djl.modality.Classifications
import ai.djl.repository.zoo.Criteria
import ai.djl.training.util.ProgressBar
import ai.djl.huggingface.translator.{TextClassificationTranslatorFactory, TextEmbeddingTranslatorFactory}
import scala.io.AnsiColor.{BLUE, BOLD, CYAN, GREEN, MAGENTA, RED, RESET, UNDERLINED, YELLOW}

val criteria =
  Criteria.builder
    .setTypes(classOf[String], classOf[Classifications])
    .optModelUrls("djl://ai.djl.huggingface.pytorch/distilbert-base-uncased-finetuned-sst-2-english")
    .optEngine("PyTorch")
    .optTranslatorFactory(new TextClassificationTranslatorFactory)
    .optProgress(new ProgressBar)
    .build

val model     = criteria.loadModel()
val predictor = model.newPredictor()

def analyze(text: String): Unit = {
  println(s"${GREEN}====================$text====================${RESET}")
  val result = predictor.predict(text)
  println(result)
}

analyze("Hello world")
analyze("I like you")
analyze("This an awful shirt")
analyze("You are a fucking bastard")
analyze("This is not very good for you but it can help")