Chinese BertForSequenceClassification Cased model (from celtics1863)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. env-bert-topic is a Chinese model originally trained by celtics1863.

Predicted Entities

邻居噪音, 空气污染, 节能减排, 环保组织, 噪音, 自来水, 环评工程师, 碳金融, 穹顶之下（纪录片）, 环境污染, 地球一小时, 净水器, 野生动物保护, 雾霾, 雾霾治理, 建筑节能, 低碳, 化学污染, PM 2.5, 垃圾处理, 水污染, 噪音污染, 沙尘暴, 植树, 垃圾分类, 环境科学, 环境评估, 核污染, 核能, 污染治理, 物种多样性, 自然环境, 二氧化碳, 沙漠治理, 水处理工程, 水处理, 风能及风力发电, 重金属污染, 温室效应, 生活, 垃圾焚烧, 环保督查, 环保行业, 生物多样性, 净水设备, 生态环境, 环境伦理学, 污水处理, 工业污染, 土壤污染, 垃圾处理器, 气候变化, 环境工程, 太阳能, 全球变暖, 碳交易, 绿色建筑, 巴黎协定, 环境保护, 环境评价, 垃圾, 碳排放, 环保经济, 污水排放, 太空垃圾, 温室气体, 净水, 秸秆焚烧, 室内空气污染

Live Demo Open in Colab Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")

seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_env_topic","zh") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler()
      .setInputCols(Array("text"))
      .setOutputCols(Array("document"))

val tokenizer = new Tokenizer()
    .setInputCols("document")
    .setOutputCol("token")

val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_env_topic","zh")
    .setInputCols(Array("document", "token"))
    .setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier))

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

Model Information

References

• https://huggingface.co/celtics1863/env-bert-topic