Description
This is a medium (md) Assertion Status Model aimed to detect temporality (PRESENT, PAST, FUTURE) or Certainty (POSSIBLE) in your financial documents, which may improve the results of the finassertion_time (small) model.
Predicted Entities
PRESENT, PAST, FUTURE, POSSIBLE
How to use
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = nlp.Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
ner = finance.BertForTokenClassification.pretrained("finner_bert_roles","en","finance/models")\
.setInputCols("token", "document")\
.setOutputCol("ner")\
.setCaseSensitive(True)
chunk_converter = nlp.NerConverter() \
.setInputCols(["document", "token", "ner"]) \
.setOutputCol("ner_chunk")
assertion = finance.AssertionDLModel.pretrained("finassertion_time_md", "en", "finance/models")\
.setInputCols(["document", "ner_chunk", "embeddings"]) \
.setOutputCol("assertion")
nlpPipeline = nlp.Pipeline(stages=[
document_assembler,
tokenizer,
embeddings,
ner,
chunk_converter,
assertion
])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)
lp = nlp.LightPipeline(model)
texts = ["John Crawford will be hired by Atlantic Inc as CTO"]
lp.annotate(texts)
Results
chunk,begin,end,entity_type,assertion
CTO,47,49,ROLE,FUTURE
Model Information
| Model Name: | finassertiondl_time_md |
| Compatibility: | Finance NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, doc_chunk, embeddings] |
| Output Labels: | [assertion] |
| Language: | en |
| Size: | 2.2 MB |
References
In-house annotations on financial and legal corpora
Benchmarking
label tp fp fn prec rec f1
PRESENT 115 11 5 0.9126984 0.9583333 0.9349593
POSSIBLE 79 5 4 0.9404762 0.9518072 0.9461077
PAST 54 5 11 0.91525424 0.83076924 0.8709678
FUTURE 77 3 4 0.9625 0.9506173 0.95652175
Macro-average 325 24 24 0.9327322 0.9228818 0.92778087
Micro-average 325 24 24 0.9312321 0.9312321 0.9312321