Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. CL_1 is a English model originally trained by yuan1729.
Predicted Entities
搶奪強盜及海盜罪, 藏匿人犯及湮滅證據罪, 賭博罪, 侵占罪, 遺棄罪, 恐嚇及擄人勒贖罪, 殺人罪, 妨害秩序罪, 偽證及誣告罪, 妨害電腦使用罪, 妨害風化罪, 瀆職罪, 妨害婚姻及家庭罪, 竊盜罪, 妨害名譽及信用罪, 傷害罪, 妨害性自主罪, 贓物罪, 妨害自由罪, 妨害秘密罪, 妨害公務罪, 詐欺背信及重利罪, 妨害投票罪, 偽造文書印文罪, 偽造有價證券罪, 公共危險罪, 毀棄損壞罪
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_cl_1","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded])
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(Array("document"))
.setOutputCol("token")
val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_cl_1","en")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded))
val data = Seq("PUT YOUR STRING HERE").toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | bert_classifier_cl_1 |
| Compatibility: | Spark NLP 4.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 383.8 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/yuan1729/CL_1