Skip to content

Commit

Permalink
2023-11-11-finner_aspect_based_sentiment_md_en (#754)
Browse files Browse the repository at this point in the history 
* Add model 2023-11-11-finner_aspect_based_sentiment_md_en

* Add model 2023-11-11-finassertion_aspect_based_sentiment_md_en

* Update 2023-11-11-finner_aspect_based_sentiment_md_en.md

* Update 2023-11-11-finassertion_aspect_based_sentiment_md_en.md

---------

Co-authored-by: Mary-Sci <meryemyildiz366@gmail.com>
Co-authored-by: Merve Ertas Uslu <67653613+Mary-Sci@users.noreply.github.com>
  • Loading branch information
@jsl-models @Mary-Sci
3 people committed Nov 11, 2023
1 parent fdca733 commit 7cc190d
Show file tree
Hide file tree
Showing 2 changed files with 267 additions and 0 deletions.
131 changes: 131 additions & 0 deletions docs/_posts/Mary-Sci/2023-11-11-finassertion_aspect_based_sentiment_md_en.md
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,131 @@
---
layout: model
title: Financial Assertion of Aspect-Based Sentiment (md, Medium)
author: John Snow Labs
name: finassertion_aspect_based_sentiment_md
date: 2023-11-11
tags: [assertion, licensed, en, finance]
task: Assertion Status
language: en
edition: Finance NLP 1.0.0
spark_version: 3.0
supported: true
annotator: AssertionDLModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This assertion model classifies financial entities into an aspect-based sentiment. It is designed to be used together with the associated NER model.

## Predicted Entities

`POSITIVE`, `NEGATIVE`, `NEUTRAL`

{:.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/finassertion_aspect_based_sentiment_md_en_1.0.0_3.0_1699705705778.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/finance/models/finassertion_aspect_based_sentiment_md_en_1.0.0_3.0_1699705705778.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
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")

# Sentence Detector annotator, processes various sentences per line
sentenceDetector = nlp.SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")

# Tokenizer splits words in a relevant format for NLP
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")

bert_embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base", "en")\
.setInputCols("sentence", "token")\
.setOutputCol("embeddings")\
.setMaxSentenceLength(512)

finance_ner = finance.NerModel.pretrained("finner_aspect_based_sentiment_md", "en", "finance/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")

ner_converter = finance.NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")

assertion_model = finance.AssertionDLModel.pretrained("finassertion_aspect_based_sentiment_md", "en", "finance/models")\
.setInputCols(["sentence", "ner_chunk", "embeddings"])\
.setOutputCol("assertion")


nlpPipeline = nlp.Pipeline(
stages=[documentAssembler,
sentenceDetector,
tokenizer,
bert_embeddings,
finance_ner,
ner_converter,
assertion_model])

text = "Equity and earnings of affiliates in Latin America increased to $4.8 million in the quarter from $2.2 million in the prior year as the commodity markets in Latin America remain strong through the end of the quarter."

spark_df = spark.createDataFrame([[text]]).toDF("text")

result = nlpPipeline.fit(spark_df ).transform(spark_df)

result.select(F.explode(F.arrays_zip("ner_chunk.result", "ner_chunk.metadata", "assertion.result", "assertion.metadata")).alias("cols"))\
.select(F.expr("cols['0']").alias("entity"),
F.expr("cols['1']['entity']").alias("label"),
F.expr("cols['2']").alias("assertion"),
F.expr("cols['3']['confidence']").alias("confidence")).show(50, truncate=False)
```

</div>

## Results

```bash
+--------+---------+---------+----------+
|entity |label |assertion|confidence|
+--------+---------+---------+----------+
|Equity |LIABILITY|POSITIVE |0.9895 |
|earnings|PROFIT |POSITIVE |0.995 |
+--------+---------+---------+----------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|finassertion_aspect_based_sentiment_md|
|Compatibility:|Finance NLP 1.0.0+|
|License:|Licensed|
|Edition:|Official|
|Input Labels:|[document, chunk, embeddings]|
|Output Labels:|[assertion]|
|Language:|en|
|Size:|2.7 MB|

## Benchmarking

```bash
label precision recall f1-score support
NEGATIVE 0.68 0.43 0.53 232
NEUTRAL 0.44 0.65 0.53 441
POSITIVE 0.79 0.69 0.74 947
accuracy - - 0.64 1620
macro-avg 0.64 0.59 0.60 1620
weighted-avg 0.68 0.64 0.65 1620
```
136 changes: 136 additions & 0 deletions docs/_posts/Mary-Sci/2023-11-11-finner_aspect_based_sentiment_md_en.md
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,136 @@
---
layout: model
title: Financial NER on Aspect-Based Sentiment Analysis
author: John Snow Labs
name: finner_aspect_based_sentiment_md
date: 2023-11-11
tags: [ner, licensed, finance, en]
task: Named Entity Recognition
language: en
edition: Finance NLP 1.0.0
spark_version: 3.0
supported: true
annotator: FinanceNerModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This NER model identifies entities that can be associated with a financial sentiment. The model is designed to be used with the associated Assertion Status model that classifies the entities into a sentiment category.

## Predicted Entities

`ASSET`, `CASHFLOW`, `EXPENSE`, `FREE_CASH_FLOW`, `GAINS`, `KPI`, `LIABILITY`, `LOSSES`, `PROFIT`, `REVENUE`

{:.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/finner_aspect_based_sentiment_md_en_1.0.0_3.0_1699704469251.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/finance/models/finner_aspect_based_sentiment_md_en_1.0.0_3.0_1699704469251.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
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")

# Sentence Detector annotator, processes various sentences per line
sentenceDetector = nlp.SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")

# Tokenizer splits words in a relevant format for NLP
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")

bert_embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base", "en")\
.setInputCols("sentence", "token")\
.setOutputCol("embeddings")\
.setMaxSentenceLength(512)


ner_model = finance.NerModel().pretrained("finner_aspect_based_sentiment_md", "en", "finance/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")

ner_converter = nlp.NerConverter()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")

nlpPipeline = nlp.Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
bert_embeddings,
ner_model,
ner_converter])

empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)

text = ["""Equity and earnings of affiliates in Latin America increased to $4.8 million in the quarter from $2.2 million in the prior year as the commodity markets in Latin America remain strong through the end of the quarter."""]
result = model.transform(spark.createDataFrame([text]).toDF("text"))

from pyspark.sql import functions as F

result.select(F.explode(F.arrays_zip(result.ner_chunk.result, result.ner_chunk.begin, result.ner_chunk.end, result.ner_chunk.metadata)).alias("cols")) \
.select(F.expr("cols['0']").alias("chunk"),
F.expr("cols['1']").alias("begin"),
F.expr("cols['2']").alias("end"),
F.expr("cols['3']['entity']").alias("ner_label")
).show(100, truncate=False)
```

</div>

## Results

```bash
+--------+-----+---+---------+
|chunk |begin|end|ner_label|
+--------+-----+---+---------+
|Equity |1 |6 |LIABILITY|
|earnings|12 |19 |PROFIT |
+--------+-----+---+---------+
```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|finner_aspect_based_sentiment_md|
|Compatibility:|Finance NLP 1.0.0+|
|License:|Licensed|
|Edition:|Official|
|Input Labels:|[sentence, token, embeddings]|
|Output Labels:|[ner]|
|Language:|en|
|Size:|16.5 MB|

## Benchmarking

```bash
label precision recall f1-score support
ASSET 0.50 0.72 0.59 53
CASHFLOW 0.78 0.60 0.68 30
EXPENSE 0.71 0.68 0.70 151
FREE_CASH_FLOW 1.00 1.00 1.00 19
GAINS 0.80 0.78 0.79 55
KPI 0.72 0.58 0.64 106
LIABILITY 0.65 0.51 0.57 39
LOSSES 0.77 0.59 0.67 29
PROFIT 0.77 0.74 0.75 101
REVENUE 0.74 0.78 0.76 231
micro-avg 0.72 0.71 0.71 814
macro-avg 0.74 0.70 0.71 814
weighted-avg 0.73 0.71 0.71 814
```

0 comments on commit 7cc190d

Please sign in to comment.