Description
This model annotates named entities in a text, that can be used to find features such as names of people, places, and organizations. The model does not read words directly but instead reads word embeddings, which represent words as points such that more semantically similar words are closer together.
This model uses the pre-trained glove_840B_300 embeddings model from WordEmbeddings annotator as an input, so be sure to use the same embeddings in the pipeline.
Predicted Entities
PER, LOC, ORG, OBJ, O
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols("sentence")\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("glove_840B_300", "xx")\
.setInputCols("document", "token") \
.setOutputCol("embeddings")
ner = NerDLModel.pretrained("ner_jifs_glove_840B_300d", "bn") \
.setInputCols(["document", "token", "embeddings"]) \
.setOutputCol("ner")
pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings, ner])
example = spark.createDataFrame([["৯০ এর দশকের শুরুর দিকে বৃহৎ আকারে মার্কিন যুক্তরাষ্ট্রে এর প্রয়োগের প্রক্রিয়া শুরু হয়'"]], ["text"])
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val embeddings = WordEmbeddingsModel.pretrained("glove_840B_300", "xx")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val ner = NerDLModel.pretrained("ner_jifs_glove_840B_300d", "bn")
.setInputCols(Array("document", "token", "embeddings"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings, ner))
val data = Seq("৯০ এর দশকের শুরুর দিকে বৃহৎ আকারে মার্কিন যুক্তরাষ্ট্রে এর প্রয়োগের প্রক্রিয়া শুরু হয়").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["৯০ এর দশকের শুরুর দিকে বৃহৎ আকারে মার্কিন যুক্তরাষ্ট্রে এর প্রয়োগের প্রক্রিয়া শুরু হয়"]
ner_df = nlu.load('bn.ner').predict(text, output_level='token')
ner_df
Results
+-------------+-----+
|token |ner |
+-------------+-----+
|৯০ |O |
|এর |O |
|দশকের |O |
|শুরুর |O |
|দিকে |O |
|বৃহৎ |O |
|আকারে |O |
|মার্কিন |B-LOC|
|যুক্তরাষ্ট্রে|I-LOC|
|এর |O |
|প্রয়োগের |O |
|প্রক্রিয়া |O |
|শুরু |O |
|হয় |O |
|' |O |
+-------------+-----+
Model Information
| Model Name: | ner_jifs_glove_840B_300d |
| Type: | ner |
| Compatibility: | Spark NLP 2.7.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | bn |
Data Source
The model was trained on the Bengali NER data set introduced in the Journal of Intelligent & Fuzzy Systems.
Reference:
- Karim, Redwanul & Islam, M. A. & Simanto, Sazid & Chowdhury, Saif & Roy, Kalyan & Neon, Adnan & Hasan, Md & Firoze, Adnan & Rahman, Mohammad. (2019). A step towards information extraction: Named entity recognition in Bangla using deep learning. Journal of Intelligent & Fuzzy Systems. 37. 1-13. 10.3233/JIFS-179349.
Benchmarking
| | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| B-LOC | 0.81 | 0.72 | 0.76 | 2005 |
| B-OBJ | 0.66 | 0.08 | 0.13 | 573 |
| B-ORG | 0.67 | 0.31 | 0.42 | 853 |
| B-PER | 0.76 | 0.76 | 0.76 | 4035 |
| I-LOC | 0.64 | 0.52 | 0.58 | 357 |
| I-OBJ | 0.00 | 0.00 | 0.00 | 57 |
| I-ORG | 0.65 | 0.37 | 0.47 | 516 |
| I-PER | 0.76 | 0.73 | 0.74 | 1223 |
| O | 0.93 | 0.97 | 0.95 | 39499 |
| accuracy | | | 0.90 | 49118 |
| macro avg | 0.65 | 0.49 | 0.54 | 49118 |
| weighted avg | 0.89 | 0.90 | 0.89 | 49118 |