Description
This is a NER model which allows you to detect some generic entities that may require to be masked or obfuscated to be compliant with different regulations, as GDPR and CCPA. This is just an NER model, make sure you try the full De-identification pipelines available in Models Hub.
The only difference between this and finner_deid is the embeddings used.
Predicted Entities
AGE, CITY, COUNTRY, DATE, EMAIL, FAX, LOCATION-OTHER, ORG, PERSON, PHONE, PROFESSION, STATE, STREET, URL, ZIP
How to use
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
ner_model = finance.NerModel.pretrained('finner_deid_sec', "en", "finance/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = nlp.NerConverter()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
ner_model,
ner_converter])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)
text = ["""
This LICENSE AND DEVELOPMENT AGREEMENT (this Agreement) is entered into effective as of Nov. 02, 2019 (the Effective Date) by and between Bioeq IP AG, having its principal place of business at 333 Twin Dolphin Drive, Suite 600, Redwood City, CA, 94065, USA (Licensee).
"""]
res = model.transform(spark.createDataFrame([text]).toDF("text"))
Results
+-----------+----------------+
| token| ner_label|
+-----------+----------------+
| This| O|
| LICENSE| O|
| AND| O|
|DEVELOPMENT| O|
| AGREEMENT| O|
| (| O|
| this| O|
| Agreement| O|
| )| O|
| is| O|
| entered| O|
| into| O|
| effective| O|
| as| O|
| of| O|
| Nov| B-DATE|
| .| I-DATE|
| 02| I-DATE|
| ,| I-DATE|
| 2019| I-DATE|
| (| O|
| the| O|
| Effective| O|
| Date| O|
| )| O|
| by| O|
| and| O|
| between| O|
| Bioeq| O|
| IP| O|
| AG| O|
| ,| O|
| having| O|
| its| O|
| principal| O|
| place| O|
| of| O|
| business| O|
| at| O|
| 333| B-STREET|
| Twin| I-STREET|
| Dolphin| I-STREET|
| Drive| I-STREET|
| ,| O|
| Suite|B-LOCATION-OTHER|
| 600|I-LOCATION-OTHER|
| ,| O|
| Redwood| B-CITY|
| City| I-CITY|
| ,| O|
| CA| B-STATE|
| ,| O|
| 94065| B-ZIP|
| ,| O|
| USA| B-STATE|
| (| O|
| Licensee| O|
| ).| O|
+-----------+----------------+
Model Information
| Model Name: | finner_deid_sec |
| Compatibility: | Finance NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 16.4 MB |
References
In-house annotated documents with protected information
Benchmarking
label precision recall f1-score support
B-AGE 0.96 0.89 0.92 245
B-CITY 0.85 0.86 0.86 123
B-COUNTRY 0.86 0.67 0.75 36
B-DATE 0.98 0.97 0.97 2352
B-ORG 0.75 0.71 0.73 38
B-PERSON 0.97 0.94 0.95 1348
B-PHONE 0.86 0.80 0.83 86
B-PROFESSION 0.93 0.75 0.83 84
B-STATE 0.92 0.89 0.91 102
B-STREET 0.99 0.91 0.95 89
I-CITY 0.82 0.77 0.79 35
I-COUNTRY 1.00 0.50 0.67 6
I-DATE 0.96 0.95 0.96 402
I-ORG 0.71 0.86 0.77 28
I-PERSON 0.98 0.96 0.97 1240
I-PHONE 0.91 0.92 0.92 77
I-PROFESSION 0.96 0.79 0.87 70
I-STATE 1.00 0.62 0.77 8
I-STREET 0.98 0.94 0.96 188
I-ZIP 0.84 0.97 0.90 60
O 1.00 1.00 1.00 194103
accuracy - - 1.00 200762
macro-avg 0.72 0.62 0.65 200762
weighted-avg 1.00 1.00 1.00 200762