Financial English Bert Embeddings (Base, Communication texts)

Description

Financial English Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. finbert-pretrain-yiyanghkust is a English model orginally available in Hugging Face as yiyanghkust/finbert-pretrain. It was trained on the following datasets:

• Corporate Reports 10-K & 10-Q: 2.5B tokens
• Earnings Call Transcripts: 1.3B tokens
• Analyst Reports: 1.1B tokens

Live Demo Open in Colab Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")

tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")

embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler() 
.setInputCol("text") 
.setOutputCol("document")

val tokenizer = new Tokenizer() 
.setInputCols(Array("document"))
.setOutputCol("token")

val embeddings = BertEmbeddings.pretrained("bert_embeddings_finbert_pretrain_yiyanghkust","en") 
.setInputCols(Array("document", "token")) 
.setOutputCol("embeddings")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))

val data = Seq("I love Spark NLP").toDF("text")

val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("en.embed.finbert_pretrain_yiyanghkust").predict("""I love Spark NLP""")

Model Information

References

• https://huggingface.co/philschmid/finbert-pretrain-yiyanghkust
• https://arxiv.org/abs/2006.08097