Merge pull request #6421 from JohnSnowLabs/2021-11-06-bert_sequence_classifier_trec_coarse_en_VvKV3BAk3ZQ6rW1c8UfnLFni

2021-11-06-bert_sequence_classifier_trec_coarse_en

Author: maziyarpanahi

docs/_posts/murat-gunay/2021-11-06-bert_sequence_classifier_trec_coarse_en.md

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+---
+layout: model
+title: BERT Sequence Classification - Identify Trec Data Classes
+author: John Snow Labs
+name: bert_sequence_classifier_trec_coarse
+date: 2021-11-06
+tags: [bert_for_sequence_classification, trec, en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 3.3.2
+spark_version: 2.4
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is imported from `Hugging Face-models` and it is a simple base BERT model trained on the "trec" dataset.
+
+## Predicted Entities
+
+`DESC`, `ENTY`, `HUM`, `NUM`, `ABBR`, `LOC`
+
+{:.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/public/models/bert_sequence_classifier_trec_coarse_en_3.3.2_2.4_1636229841055.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequenceClassifier = BertForSequenceClassification \
+      .pretrained('bert_sequence_classifier_trec_coarse', 'en') \
+      .setInputCols(['token', 'document']) \
+      .setOutputCol('class') \
+      .setCaseSensitive(True) \
+      .setMaxSentenceLength(512)
+
+pipeline = Pipeline(stages=[document_assembler, tokenizer, sequenceClassifier])
+
+example = spark.createDataFrame([['Germany is the largest country in Europe economically.']]).toDF("text")
+result = pipeline.fit(example).transform(example)
+```
+```scala
+val document_assembler = DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val tokenizer = Tokenizer() 
+    .setInputCols("document") 
+    .setOutputCol("token")
+
+val tokenClassifier = BertForSequenceClassification("bert_sequence_classifier_trec_coarse", "en")
+      .setInputCols("document", "token")
+      .setOutputCol("class")
+      .setCaseSensitive(true)
+      .setMaxSentenceLength(512)
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
+
+val example = Seq.empty["Germany is the largest country in Europe economically."].toDS.toDF("text")
+
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+## Results
+
+```bash
+['LOC']
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_sequence_classifier_trec_coarse|
+|Compatibility:|Spark NLP 3.3.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[label]|
+|Language:|en|
+|Case sensitive:|true|
+
+## Data Source
+
+[https://huggingface.co/aychang/bert-base-cased-trec-coarse](https://huggingface.co/aychang/bert-base-cased-trec-coarse)