Finance NLP 1.8.0

docs/_posts/bunyamin-polat/2023-02-16-finclf_capital_call_notices_en.md

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+---
+layout: model
+title: Finance Capital Call Notices Document Classifier (Bert Sentence Embeddings)
+author: John Snow Labs
+name: finclf_capital_call_notices
+date: 2023-02-16
+tags: [en, licensed, finance, capital_calls, classification, tensorflow]
+task: Text Classification
+language: en
+edition: Finance NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalClassifierDLModel
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `finclf_capital_call_notices` model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class `capital_call_notices` or not (Binary Classification).
+
+## Predicted Entities
+
+`capital_call_notices`, `other`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/finance/models/finclf_capital_call_notices_en_1.0.0_3.0_1676590287518.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/finance/models/finclf_capital_call_notices_en_1.0.0_3.0_1676590287518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+document_assembler = nlp.DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
+    .setInputCols("document")\
+    .setOutputCol("sentence_embeddings")
+
+doc_classifier = finance.ClassifierDLModel.pretrained("finclf_capital_call_notices", "en", "finance/models")\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("category")
+
+nlpPipeline = nlp.Pipeline(stages=[
+    document_assembler, 
+    embeddings,
+    doc_classifier])
+
+df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")
+
+model = nlpPipeline.fit(df)
+
+result = model.transform(df)
+```
+
+</div>
+
+## Results
+
+```bash
++-------+
+|result|
++-------+
+|[capital_call_notices]|
+|[other]|
+|[other]|
+|[capital_call_notices]|
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|finclf_capital_call_notices|
+|Compatibility:|Finance NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.4 MB|
+
+## References
+
+Financial documents and classified in-house + SEC documents
+
+## Benchmarking
+
+```bash
+label                 precision  recall  f1-score  support 
+capital_call_notices  1.00       1.00    1.00      12      
+other                 1.00       1.00    1.00      23      
+accuracy              -          -       1.00      35      
+macro-avg             1.00       1.00    1.00      35      
+weighted-avg          1.00       1.00    1.00      35   
+```