Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. autonlp-cat333-624217911 is a Chinese model originally trained by kyleinincubated.
Predicted Entities
渔业, 采矿业, 公用事业, 交通运输, 农业, 电子制造, 休闲服务, 文化, 商业贸易, 畜牧业, 林业, 轻工制造, 教育, 食品饮料, 化工制造, 非银金融, 房地产, 传媒, 通信, 汽车制造, 信息技术, 有色金属, 互联网服务, 银行, 纺织服装制造, 医药生物, 钢铁, 建筑业, 电气设备
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_autonlp_cat333_624217911","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_autonlp_cat333_624217911","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
| Model Name: | bert_classifier_autonlp_cat333_624217911 |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | zh |
| Size: | 383.5 MB |
References
References
- https://huggingface.co/kyleinincubated/autonlp-cat333-624217911