Add model 2022-10-22-finclf_bert_sentiment_analysis_lt (#12975)

Co-authored-by: josejuanmartinez <jjmcarrascosa@gmail.com>

Author: jsl-models

docs/_posts/josejuanmartinez/2022-10-22-finclf_bert_sentiment_analysis_lt.md

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+---
+layout: model
+title: Financial Sentiment Analysis (Lithuanian)
+author: John Snow Labs
+name: finclf_bert_sentiment_analysis
+date: 2022-10-22
+tags: [lt, legal, classification, sentiment, analysis, licensed]
+task: Text Classification
+language: lt
+edition: Spark NLP for Finance 1.0.0
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This is a Lithuanian Sentiment Analysis Text Classifier, which will retrieve if a text is either expression a Positive Emotion or a Negative one.
+
+## Predicted Entities
+
+`APPLICANT`, `COMMISSION/CHAMBER`, `ECHR`, `OTHER`, `STATE`, `THIRD PARTIES`
+
+{:.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_bert_sentiment_analysis_lt_1.0.0_3.0_1666475378253.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+# Test classifier in Spark NLP pipeline
+document_assembler = nlp.DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = nlp.Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+# Load newly trained classifier
+sequenceClassifier_loaded = finance.BertForSequenceClassification.pretrained("finclf_bert_sentiment_analysis", "lt", "finance/models")\
+  .setInputCols(["document",'token'])\
+  .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+    document_assembler, 
+    tokenizer,
+    sequenceClassifier_loaded    
+])
+
+# Generating example
+example = spark.createDataFrame([["Pagalbos paraðiuto laukiantis verslas priemones vertina teigiamai  tik yra keli „jeigu“"]]).toDF("text")
+
+result = pipeline.fit(example).transform(example)
+
+# Checking results
+result.select("text", "class.result").show(truncate=False)
+```
+
+</div>
+
+## Results
+
+```bash
++---------------------------------------------------------------------------------------+------+
+|text                                                                                   |result|
++---------------------------------------------------------------------------------------+------+
+|Pagalbos paraðiuto laukiantis verslas priemones vertina teigiamai  tik yra keli „jeigu“|[POS] |
++---------------------------------------------------------------------------------------+------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|finclf_bert_sentiment_analysis|
+|Compatibility:|Spark NLP for Finance 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|lt|
+|Size:|406.6 MB|
+|Case sensitive:|true|
+|Max sentence length:|128|
+
+## References
+
+An in-house augmented version of [this dataset](https://www.kaggle.com/datasets/rokastrimaitis/lithuanian-financial-news-dataset-and-bigrams?select=dataset%28original%29.csv) removing NEU tag
+
+## Benchmarking
+
+```bash
+label            precision    recall  f1-score   support
+
+         NEG       0.80      0.76      0.78       509
+         POS       0.90      0.92      0.91      1167
+
+    accuracy                           0.87      1676
+   macro avg       0.85      0.84      0.84      1676
+weighted avg       0.87      0.87      0.87      1676
+```