Description
BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model is a fine-tuned on NER-C version of the Spanish BERT cased (BETO) for NER downstream task.
Predicted Entities
- B-LOC
- B-MISC
- B-ORG
- B-PER
- I-LOC
- I-MISC
- I-ORG
- I-PER
- O
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
tokenClassifier = BertForTokenClassification \
.pretrained('bert_token_classifier_spanish_ner', 'es') \
.setInputCols(['token', 'document']) \
.setOutputCol('ner') \
.setCaseSensitive(True) \
.setMaxSentenceLength(512)
# since output column is IOB/IOB2 style, NerConverter can extract entities
ner_converter = NerConverter() \
.setInputCols(['document', 'token', 'ner']) \
.setOutputCol('entities')
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
tokenClassifier,
ner_converter
])
example = spark.createDataFrame([["Me llamo Wolfgang y vivo en Berlin"]]).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 = BertForTokenClassification.pretrained("bert_token_classifier_spanish_ner", "es")
.setInputCols("document", "token")
.setOutputCol("ner")
.setCaseSensitive(true)
.setMaxSentenceLength(512)
// since output column is IOB/IOB2 style, NerConverter can extract entities
val ner_converter = NerConverter()
.setInputCols("document", "token", "ner")
.setOutputCol("entities")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, tokenClassifier, ner_converter))
val example = Seq.empty["Me llamo Wolfgang y vivo en Berlin"].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("es.classify.token_bert.spanish_ner").predict("""Me llamo Wolfgang y vivo en Berlin""")
Model Information
| Model Name: | bert_token_classifier_spanish_ner |
| Compatibility: | Spark NLP 3.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [ner] |
| Language: | es |
| Case sensitive: | false |
| Max sentense length: | 512 |
Data Source
https://huggingface.co/mrm8488/bert-spanish-cased-finetuned-ner
Benchmarking
| Metric | # score |
| :------------------------------------------------------------------------------------: | :-------: |
| F1 | **90.17**
| Precision | **89.86** |
| Recall | **90.47** |
## Comparison:
| Model | # F1 score |Size(MB)|
| :--------------------------------------------------------------------------------------------------------------: | :-------: |:------|
| bert-base-spanish-wwm-cased (BETO) | 88.43 | 421
| [bert-spanish-cased-finetuned-ner (this one)](https://huggingface.co/mrm8488/bert-spanish-cased-finetuned-ner) | **90.17** | 420 |
| Best Multilingual BERT | 87.38 | 681 |
|[TinyBERT-spanish-uncased-finetuned-ner](https://huggingface.co/mrm8488/TinyBERT-spanish-uncased-finetuned-ner) | 70.00 | **55** |