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
How to use
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)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = BertForSequenceClassification.pretrained("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)
import nlu
nlu.load("en.classify.bert.by_aychang").predict("""Germany is the largest country in Europe economically.""")
Results
['LOC']
Model Information
| 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
Benchmarking
epoch: 2.0, eval_loss: 0.138086199760437
eval_runtime: 1.6132, eval_samples_per_second: 309.94
+------------+-------+-----------------+--------------+
| entity|eval_f1| eval_precision| eval_recall|
+------------+-------+-----------------+--------------+
| DESC| 0.981| 0.985| 0.978|
| ENTY| 0.944| 0.988| 0.904|
| ABBR| 1.| 1.| 1.|
| HUM| 0.992| 0.984| 1.|
| NUM| 0.969| 0.941| 1.|
| LOC| 0.981| 0.975| 0.987|
+------------+-------+-----------------+--------------+
eval_accuracy: 0.974