Description
Pretrained Finance BERT Sentence Embedding model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. sbert-chinese-qmc-finance-v1-distill is a Chinese model originally trained by DMetaSoul.
How to use
sent_embeddings = BertSentenceEmbeddings.pretrained("bert_sentence_embeddings_sbert_chinese_qmc_finance_v1_distill", "zh") \
.setInputCols("sentence") \
.setOutputCol("bert_sentence")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, sent_embeddings ])
result = pipeline.fit(data).transform(data)
val sent_embeddings = BertSentenceEmbeddings.pretrained("bert_sentence_embeddings_sbert_chinese_qmc_finance_v1_distill", "zh")
.setInputCols("sentence")
.setOutputCol("bert_sentence")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, sent_embeddings ))
Model Information
| Model Name: | bert_sentence_embeddings_sbert_chinese_qmc_finance_v1_distill |
| Compatibility: | Spark NLP 4.3.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence] |
| Output Labels: | [bert_sentence] |
| Language: | zh |
| Size: | 171.0 MB |
| Case sensitive: | true |
References
- https://huggingface.co/DMetaSoul/sbert-chinese-qmc-finance-v1-distill
- https://www.SBERT.net