Description
This is a Multiclass classification model which retrieves the topic/class of an informal message from a legal forum, including the following classes: digital, business, insurance, contract, driving, school, family, wills, employment, housing, criminal.
Predicted Entities
digital, business, insurance, contract, driving, school, family, wills, employment, housing
How to use
documentAssembler = nlp.DocumentAssembler() \
.setInputCols(["text"]) \
.setOutputCols("document")
tokenizer = nlp.Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = legal.BertForSequenceClassification.pretrained("legclf_reddit_advice", "en", "legal/models") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = nlp.Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])
data = spark.createDataFrame([["Mother of my child took my daughter and moved (without notice), won't let me see her or tell me where she is."]]).toDF("text")
result = pipeline.fit(data).transform(data)
Results
+--------+
| result|
+--------+
|[family]|
+--------+
Model Information
| Model Name: | legclf_reddit_advice |
| Compatibility: | Legal NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 406.4 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |
References
Train dataset available here
Benchmarking
label precision recall f1-score support
business 0.76 0.67 0.72 239
contract 0.80 0.68 0.73 207
criminal 0.82 0.77 0.80 209
digital 0.76 0.74 0.75 223
driving 0.86 0.85 0.86 223
employment 0.76 0.92 0.83 222
family 0.88 0.95 0.92 216
housing 0.89 0.95 0.92 221
insurance 0.83 0.80 0.81 221
school 0.87 0.91 0.89 207
wills 0.95 0.96 0.96 199
accuracy - - 0.83 2387
macro-avg 0.84 0.84 0.83 2387
weighted-avg 0.83 0.83 0.83 2387