Description
A BERT model pre-trained on 17 Indian languages, and their transliterated counterparts.
This model uses a BERT base architecture 1 pretrained from scratch using the Wikipedia 2, Common Crawl 3, PMINDIA 4 and Dakshina 5 corpora for the following 17 Indian languages:
Assamese, Bengali , English , Gujarati , Hindi , Kannada , Kashmiri , Malayalam , Marathi , Nepali , Oriya , Punjabi , Sanskrit , Sindhi , Tamil , Telugu , Urdu
How to use
embeddings = BertEmbeddings.pretrained("bert_muril", "xx") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
val embeddings = BertEmbeddings.pretrained("bert_muril", "xx")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
import nlu
nlu.load("xx.embed.bert.muril").predict("""Put your text here.""")
Model Information
| Model Name: | bert_muril |
| Compatibility: | Spark NLP 3.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | xx |
| Case sensitive: | false |
Data Source
The model is imported from: https://tfhub.dev/google/MuRIL/1