Description
This model is a Named Entity Recognition (NER) model focused on financial numeric items. It identifies 11 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
TreasuryStockAcquiredAverageCostPerShare, StockRepurchasedDuringPeriodShares, StockRepurchaseProgramAuthorizedAmount1, TreasuryStockSharesAcquired, StockRepurchasedAndRetiredDuringPeriodShares, RepaymentsOfDebt, CommonStockDividendsPerShareDeclared, StockRepurchaseProgramRemainingAuthorizedRepurchaseAmount1, DebtInstrumentRedemptionPricePercentage, PreferredStockDividendRatePercentage, TreasuryStockValueAcquiredCostMethod
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_lg_contra_liability', 'en', 'finance/models')\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
pipeline = nlp.Pipeline(stages=[documentAssembler,
sentence,
tokenizer,
embeddings,
nerTagger
])
text = "Any optional redemption of the Notes will be at a redemption price equal to 100 % of the principal amount of the Notes to be redeemed , plus accrued and unpaid interest to , but excluding , the redemption date . "
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|
+----------+-----------------------------------------+----------+
|Any |O |1.0 |
|optional |O |1.0 |
|redemption|O |1.0 |
|of |O |1.0 |
|the |O |1.0 |
|Notes |O |1.0 |
|will |O |1.0 |
|be |O |1.0 |
|at |O |1.0 |
|a |O |1.0 |
|redemption|O |1.0 |
|price |O |1.0 |
|equal |O |1.0 |
|to |O |1.0 |
|100 |B-DebtInstrumentRedemptionPricePercentage|0.9999 |
|% |O |1.0 |
|of |O |1.0 |
|the |O |1.0 |
|principal |O |1.0 |
|amount |O |1.0 |
|of |O |1.0 |
|the |O |1.0 |
|Notes |O |1.0 |
|to |O |1.0 |
|be |O |1.0 |
|redeemed |O |1.0 |
|, |O |1.0 |
|plus |O |1.0 |
|accrued |O |1.0 |
|and |O |1.0 |
|unpaid |O |1.0 |
|interest |O |1.0 |
|to |O |1.0 |
|, |O |1.0 |
|but |O |1.0 |
|excluding |O |1.0 |
|, |O |1.0 |
|the |O |1.0 |
|redemption|O |1.0 |
|date |O |1.0 |
|. |O |1.0 |
+----------+-----------------------------------------+----------+
Model Information
| Model Name: | finner_10q_xbrl_lg_contra_liability |
| 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
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-CommonStockDividendsPerShareDeclared 0.9455 0.9975 0.9708 400
B-DebtInstrumentRedemptionPricePercentage 0.9944 0.9806 0.9874 360
B-PreferredStockDividendRatePercentage 0.9600 1.0000 0.9796 144
B-RepaymentsOfDebt 0.9310 0.9586 0.9446 169
B-StockRepurchaseProgramAuthorizedAmount1 0.9653 0.9430 0.9540 561
B-StockRepurchaseProgramRemainingAuthorizedRepurchaseAmount1 0.9099 0.9670 0.9376 303
B-StockRepurchasedAndRetiredDuringPeriodShares 0.7500 0.4717 0.5792 159
B-StockRepurchasedDuringPeriodShares 0.5323 0.1579 0.2435 209
B-TreasuryStockAcquiredAverageCostPerShare 0.7884 0.9744 0.8716 195
B-TreasuryStockSharesAcquired 0.5664 0.9107 0.6984 403
B-TreasuryStockValueAcquiredCostMethod 0.6218 0.3304 0.4315 224
I-TreasuryStockSharesAcquired 0.0000 0.0000 0.0000 1
O 0.9981 0.9979 0.9980 92921
accuracy - - 0.9927 96049
macro-avg 0.7664 0.7453 0.7382 96049
weighted-avg 0.9926 0.9927 0.9921 96049