Description
RobertaNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from ARMAN, PEYMA, and WikiANN that covered ten types of entities:
- Date (DAT)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Money (MON)
- Organization (ORG)
- Percent (PCT)
- Person (PER)
- Product (PRO)
- Time (TIM)
Dataset Information
| Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 |
| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 |
| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 |
Predicted Entities
DAT, EVE, FAC, LOC, MON, ORG, PCT, PER, PRO, TIM
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
tokenClassifier = RoBertaForTokenClassification \
.pretrained('roberta_token_classifier_zwnj_base_ner', 'fa') \
.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([['در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند.']]).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 = RoBertaForTokenClassification.pretrained("roberta_token_classifier_zwnj_base_ner", "fa")
.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["در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("fa.classify.token_roberta_token_classifier_zwnj_base_ner").predict("""در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند.""")
Model Information
| Model Name: | roberta_token_classifier_zwnj_base_ner |
| Compatibility: | Spark NLP 3.3.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [ner] |
| Language: | fa |
| Case sensitive: | true |
| Max sentense length: | 512 |
Data Source
https://huggingface.co/HooshvareLab/roberta-fa-zwnj-base-ner
Benchmarking
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
**Overall**
| Model | accuracy | precision | recall | f1 |
|:----------:|:--------:|:---------:|:--------:|:--------:|
| Roberta | 0.994849 | 0.949816 | 0.960235 | 0.954997 |
**Per entities**
| | number | precision | recall | f1 |
|:---: |:------: |:---------: |:--------: |:--------: |
| DAT | 407 | 0.844869 | 0.869779 | 0.857143 |
| EVE | 256 | 0.948148 | 1.000000 | 0.973384 |
| FAC | 248 | 0.957529 | 1.000000 | 0.978304 |
| LOC | 2884 | 0.965422 | 0.968100 | 0.966759 |
| MON | 98 | 0.937500 | 0.918367 | 0.927835 |
| ORG | 3216 | 0.943662 | 0.958333 | 0.950941 |
| PCT | 94 | 1.000000 | 0.968085 | 0.983784 |
| PER | 2646 | 0.957030 | 0.959562 | 0.958294 |
| PRO | 318 | 0.963636 | 1.000000 | 0.981481 |
| TIM | 43 | 0.739130 | 0.790698 | 0.764045 |