Description
This model is a Named Entity Recognition (NER) model focused on financial numeric items. It identifies 12 numeric financial entities from diverse 10-Q and 10-K reports. These entities are annotated using eXtensible Business Reporting Language (XBRL) tags. The annotation process primarily targets numerical tokens, and the context plays a crucial role in accurately assigning the appropriate entity type from the 139 most common financial entities available in the dataset.
Predicted Entities
ConcentrationRiskPercentage1, BusinessCombinationContingentConsiderationLiability, BusinessCombinationRecognizedIdentifiableAssetsAcquiredAndLiabilitiesAssumedIntangibles, BusinessCombinationRecognizedIdentifiableAssetsAcquiredAndLiabilitiesAssumedIntangibleAssetsOtherThanGoodwill, CommonStockSharesAuthorized, CommonStockSharesOutstanding, CashAndCashEquivalentsFairValueDisclosure, ClassOfWarrantOrRightExercisePriceOfWarrantsOrRights1, CommonStockParOrStatedValuePerShare, CommonStockCapitalSharesReservedForFutureIssuance, CapitalizedContractCostAmortization, CommonStockDividendsPerShareDeclared
How to use
documentAssembler = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentence = nlp.SentenceDetector() \
.setInputCols(["document"]) \
.setOutputCol("sentence")
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")\
.setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '”', '’', '$','€'])
embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")\
.setMaxSentenceLength(512)
nerTagger = finance.NerModel.pretrained('finner_10q_xbrl_md_subset2', 'en', 'finance/models')\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
pipeline = nlp.Pipeline(stages=[documentAssembler,
sentence,
tokenizer,
embeddings,
nerTagger
])
text = "Common Stock The authorized capital of the Company is 200,000,000 common shares , par value $ 0.001 , of which 12,481,724 are issued or outstanding ."
df = spark.createDataFrame([[text]]).toDF("text")
fit = pipeline.fit(df)
result = fit.transform(df)
result_df = result.select(F.explode(F.arrays_zip(result.token.result,result.ner.result, result.ner.metadata)).alias("cols"))\
.select(F.expr("cols['0']").alias("token"),\
F.expr("cols['1']").alias("ner_label"),\
F.expr("cols['2']['confidence']").alias("confidence"))
result_df.show(50, truncate=100)
Results
+-----------+-------------------------------------------------------+----------+
|token |ner_label |confidence|
+-----------+-------------------------------------------------------+----------+
|The |O |1.0 |
|Warrant |O |1.0 |
|bears |O |1.0 |
|a |O |1.0 |
|purchase |O |1.0 |
|price |O |1.0 |
|of |O |1.0 |
|$ |O |1.0 |
|3.17 |B-ClassOfWarrantOrRightExercisePriceOfWarrantsOrRights1|0.9582 |
|per |O |1.0 |
|share |O |1.0 |
|, |O |1.0 |
|subject |O |1.0 |
|to |O |1.0 |
|adjustments|O |1.0 |
|. |O |1.0 |
+-----------+-------------------------------------------------------+----------+
Model Information
| Model Name: | finner_10q_xbrl_md_subset2 |
| Compatibility: | Finance NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 16.5 MB |
References
An in-house modified version of https://huggingface.co/datasets/nlpaueb/finer-139, re-splited and filtered to focus on sentences with bigger density of tags.
Benchmarking
label precision recall f1-score support
B-BusinessCombinationContingentConsiderationLiability 0.9127 0.9914 0.9504 232
B-BusinessCombinationRecognizedIdentifiableAssetsAcquiredAndLiabilitiesAssumedIntangibleAssetsOtherThanGoodwill 0.7333 0.8148 0.7719 81
B-BusinessCombinationRecognizedIdentifiableAssetsAcquiredAndLiabilitiesAssumedIntangibles 0.7907 0.6182 0.6939 55
B-CapitalizedContractCostAmortization 0.9829 1.0000 0.9914 230
B-CashAndCashEquivalentsFairValueDisclosure 1.0000 0.9920 0.9960 250
B-ClassOfWarrantOrRightExercisePriceOfWarrantsOrRights1 0.9873 1.0000 0.9936 156
B-CommonStockCapitalSharesReservedForFutureIssuance 0.9353 0.9938 0.9636 160
B-CommonStockDividendsPerShareDeclared 0.9651 1.0000 0.9822 332
B-CommonStockParOrStatedValuePerShare 0.9766 0.9709 0.9738 172
B-CommonStockSharesAuthorized 0.9817 0.9583 0.9699 168
B-CommonStockSharesOutstanding 0.9796 0.9172 0.9474 157
B-ConcentrationRiskPercentage1 0.9945 0.9899 0.9922 1091
I-CommonStockSharesAuthorized 0.0000 0.0000 0.0000 3
O 0.9995 0.9992 0.9993 76729
accuracy - - 0.9982 79816
macro-avg 0.8742 0.8747 0.8733 79816
weighted-avg 0.9982 0.9982 0.9982 79816