diff --git a/docs/_posts/Mary-Sci/2023-08-04-legclf_law_stack_exchange_en.md b/docs/_posts/Mary-Sci/2023-08-04-legclf_law_stack_exchange_en.md new file mode 100644 index 0000000000..e48a7fd350 --- /dev/null +++ b/docs/_posts/Mary-Sci/2023-08-04-legclf_law_stack_exchange_en.md @@ -0,0 +1,117 @@ +--- +layout: model +title: Legal Law Stack Exchange Classifier in Domain-Specific Documents +author: John Snow Labs +name: legclf_law_stack_exchange +date: 2023-08-04 +tags: [en, licensed, classification, legal, tensorflow] +task: Text Classification +language: en +edition: Legal NLP 1.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: LegalBertForSequenceClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This model is a multi-class classification model that can classify a wide variety of legal issues. The model demonstrates remarkable proficiency in predicting `business`, `constitutional-law`, `contract-law`, `copyright`, `criminal-law`, `employment`, `liability`, `privacy`, `tax-law`, and `trademark`. + +## Predicted Entities + +`business`, `constitutional-law`, `contract-law`, `copyright`, `criminal-law`, `employment`, `liability`, `privacy`, `tax-law`, `trademark` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/legal/models/legclf_law_stack_exchange_en_1.0.0_3.0_1691173181059.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legclf_law_stack_exchange_en_1.0.0_3.0_1691173181059.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +document_assembler = nlp.DocumentAssembler() \ + .setInputCol('text') \ + .setOutputCol('document') + +tokenizer = nlp.Tokenizer() \ + .setInputCols(['document']) \ + .setOutputCol('token') + +sequenceClassifier = legal.BertForSequenceClassification.pretrained("legclf_law_stack_exchange", "en", "legal/models") \ + .setInputCols(["document", "token"]) \ + .setOutputCol("class") + +pipeline = nlp.Pipeline(stages=[ + document_assembler, + tokenizer, + sequenceClassifier +]) + +# couple of simple examples +example = spark.createDataFrame([["I have been helping a nonprofit by developing a piece of software that they needed. The software is more-or-less built to their specs in a 'functional' way, but I wrote 100% of the code: they are not programmers. Anyhow, we didn't make any kind of contract at the beginning verbally or otherwise. Who owns the copyright to all of this? Do they have any rights to it at all for providing 'ideas'?"]]).toDF("text") + +result = pipeline.fit(example).transform(example) + +# result is a DataFrame +result.select("text", "class.result").show(truncate=100) +``` + +
+ +## Results + +```bash ++----------------------------------------------------------------------------------------------------+-----------+ +| text| result| ++----------------------------------------------------------------------------------------------------+-----------+ +|I have been helping a nonprofit by developing a piece of software that they needed. The software ...|[copyright]| ++----------------------------------------------------------------------------------------------------+-----------+ +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legclf_law_stack_exchange| +|Compatibility:|Legal NLP 1.0.0+| +|License:|Licensed| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[class]| +|Language:|en| +|Size:|410.1 MB| +|Case sensitive:|true| +|Max sentence length:|512| + +## References + +Train dataset available [here](https://huggingface.co/datasets/jonathanli/law-stack-exchange) + +## Benchmarking + +```bash +label precision recall f1-score support +business 0.50 0.24 0.32 17 +constitutional-law 0.94 0.68 0.79 25 +contract-law 0.88 0.85 0.86 91 +copyright 0.91 0.97 0.94 151 +criminal-law 0.80 0.91 0.85 75 +employment 0.74 0.93 0.82 30 +liability 0.67 0.31 0.42 13 +privacy 0.77 0.82 0.79 28 +tax-law 0.93 0.78 0.85 32 +trademark 0.89 0.91 0.90 44 +accuracy - - 0.86 506 +macro-avg 0.80 0.74 0.75 506 +weighted-avg 0.85 0.86 0.85 506 +``` \ No newline at end of file diff --git a/docs/_posts/gadde5300/2023-08-07-legner_bert_subpoenas_sm_en.md b/docs/_posts/gadde5300/2023-08-07-legner_bert_subpoenas_sm_en.md new file mode 100644 index 0000000000..d60b1a71f5 --- /dev/null +++ b/docs/_posts/gadde5300/2023-08-07-legner_bert_subpoenas_sm_en.md @@ -0,0 +1,165 @@ +--- +layout: model +title: Legal NER on Subpoenas (Small) +author: John Snow Labs +name: legner_bert_subpoenas_sm +date: 2023-08-07 +tags: [en, licensed, tensorflow] +task: Named Entity Recognition +language: en +edition: Legal NLP 1.0.0 +spark_version: 3.0 +supported: true +engine: tensorflow +annotator: LegalBertForTokenClassification +article_header: + type: cover +use_language_switcher: "Python-Scala-Java" +--- + +## Description + +This is a Legal NER model aimed to extract 19 entities from subpoenas. This is called a small version because it has been trained on more generic labels. The larger versions of this model will be available on models hub. + +## Predicted Entities + +`COURT`, `APPOINTMENT_DATE`, `DEADLINE_DATE`, `DOCUMENT_DATE_FROM`, `ADDRESS`, `APPOINTMENT_HOUR`, `DOCUMENT_DATE_TO`, `DOCUMENT_PERSON`, `DOCUMENT_DATE_YEAR`, `STATE`, `MATTER_VS`, `CASE`, `COUNTY`, `DOCUMENT_TOPIC`, `MATTER`, `SUBPOENA_DATE`, `SIGNER`, `RECEIVER`, `DOCUMENT_TYPE` + +{:.btn-box} + + +[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/legal/models/legner_bert_subpoenas_sm_en_1.0.0_3.0_1691423741988.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden} +[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legner_bert_subpoenas_sm_en_1.0.0_3.0_1691423741988.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} + +## How to use + + + +
+{% include programmingLanguageSelectScalaPythonNLU.html %} +```python +from pyspark.sql import functions as F + +document_assembler = nlp.DocumentAssembler()\ + .setInputCol("text")\ + .setOutputCol("document")\ + +sentence_detector = nlp.SentenceDetector()\ + .setInputCols(["document"])\ + .setOutputCol("sentence")\ + +tokenizer = nlp.Tokenizer() \ + .setInputCols(["sentence"]) \ + .setOutputCol("token") + +ner_model = legal.BertForTokenClassification.pretrained("legner_bert_subpoenas_sm", "en", "legal/models")\ + .setInputCols(["sentence", "token"])\ + .setOutputCol("ner")\ + .setCaseSensitive(True)\ + .setMaxSentenceLength(512) + +ner_converter = nlp.NerConverter()\ + .setInputCols(["sentence", "token", "ner"])\ + .setOutputCol("ner_chunk") + +pipeline = nlp.Pipeline(stages=[ + document_assembler, + sentence_detector, + tokenizer, + ner_model, + ner_converter +]) + + +empty_data = spark.createDataFrame([[""]]).toDF("text") + +model = pipeline.fit(empty_data) + +text = """In addition , in an earlier motion for summary disposition in which all Respondents joined , and which this Court denied in its Order of April30 , 2013 , Respondent Deloitte Touche Tohmatsu Certified Public Accountants Ltd .""" +data = spark.createDataFrame([[text]]).toDF("text") + +result = model.transform(data) + +result.select(F.explode(F.arrays_zip('ner_chunk.result', 'ner_chunk.metadata')).alias("cols")) \ + .select(F.expr("cols['0']").alias("chunk"), + F.expr("cols['1']['entity']").alias("label")).show(50, truncate = False) +``` + +
+ +## Results + +```bash ++------------------------+---------------+ +|chunk |label | ++------------------------+---------------+ +|summary disposition |DOCUMENT_TYPE | +|Deloitte Touche Tohmatsu|DOCUMENT_PERSON| ++------------------------+---------------+ +``` + +{:.model-param} +## Model Information + +{:.table-model} +|---|---| +|Model Name:|legner_bert_subpoenas_sm| +|Compatibility:|Legal NLP 1.0.0+| +|License:|Licensed| +|Edition:|Official| +|Input Labels:|[document, token]| +|Output Labels:|[ner]| +|Language:|en| +|Size:|401.1 MB| +|Case sensitive:|true| +|Max sentence length:|128| + +## References + +In House annotated dataset + +## Benchmarking + +```bash +label precision recall f1-score support + B-COURT 1.00 0.60 0.75 30 + I-APPOINTMENT_DATE 0.57 0.65 0.60 20 + I-COURT 0.93 0.89 0.91 166 + B-APPOINTMENT_DATE 0.67 0.44 0.53 9 + I-DEADLINE_DATE 0.83 0.26 0.40 19 +B-DOCUMENT_DATE_FROM 0.80 1.00 0.89 16 + I-ADDRESS 0.87 0.94 0.90 1046 + B-APPOINTMENT_HOUR 0.43 0.92 0.59 13 + B-DOCUMENT_DATE_TO 0.88 1.00 0.93 7 + I-APPOINTMENT_HOUR 1.00 0.15 0.26 20 + B-DOCUMENT_PERSON 0.79 0.84 0.82 2919 +B-DOCUMENT_DATE_YEAR 0.00 0.00 0.00 5 + B-STATE 0.59 0.79 0.68 24 + I-MATTER_VS 0.65 0.79 0.71 150 + I-CASE 0.00 0.00 0.00 11 + I-COUNTY 0.00 0.00 0.00 0 + B-DOCUMENT_TOPIC 0.64 0.77 0.70 208 + B-COUNTY 0.00 0.00 0.00 0 + B-MATTER 0.85 0.86 0.86 328 +I-DOCUMENT_DATE_FROM 0.87 1.00 0.93 48 + I-SUBPOENA_DATE 0.56 0.28 0.38 53 + I-SIGNER 0.56 0.46 0.50 59 + I-DOCUMENT_DATE_TO 0.83 1.00 0.91 25 + I-RECEIVER 0.71 0.52 0.60 98 + B-SIGNER 0.76 0.49 0.59 39 + I-DOCUMENT_TOPIC 0.83 0.80 0.81 725 + I-STATE 0.67 0.29 0.40 14 + B-MATTER_VS 0.78 0.82 0.80 136 + I-DOCUMENT_TYPE 0.83 0.87 0.85 621 + B-DEADLINE_DATE 0.00 0.00 0.00 6 + I-MATTER 0.88 0.82 0.85 479 + B-DOCUMENT_TYPE 0.87 0.90 0.88 1714 + B-ADDRESS 0.81 0.83 0.82 101 + B-SUBPOENA_DATE 0.42 0.28 0.33 18 + B-CASE 0.91 0.97 0.94 312 + I-DOCUMENT_PERSON 0.80 0.83 0.81 3672 + B-RECEIVER 0.76 0.63 0.69 46 + micro-avg 0.82 0.84 0.83 13157 + macro-avg 0.66 0.61 0.61 13157 + weighted-avg 0.82 0.84 0.83 13157 +```