Description
Pretrained BertSentenceEmbeddings, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.sent_bert_base_uncased_issues_128_feng_2052_pipeline is a English model originally trained by feng-2052.
How to use
pipeline = PretrainedPipeline("sent_bert_base_uncased_issues_128_feng_2052_pipeline", lang = "en")
annotations = pipeline.transform(df)
val pipeline = new PretrainedPipeline("sent_bert_base_uncased_issues_128_feng_2052_pipeline", lang = "en")
val annotations = pipeline.transform(df)
Model Information
| Model Name: | sent_bert_base_uncased_issues_128_feng_2052_pipeline |
| Type: | pipeline |
| Compatibility: | Spark NLP 5.5.1+ |
| License: | Open Source |
| Edition: | Official |
| Language: | en |
| Size: | 407.7 MB |
References
https://huggingface.co/feng-2052/bert-base-uncased-issues-128
Included Models
- DocumentAssembler
- TokenizerModel
- SentenceDetectorDLModel
- BertSentenceEmbeddings