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

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

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

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

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

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

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

val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

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

val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_env_topic","zh")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

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

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