Description
This is an Entity Resolution / Entity Linking model, which is able to provide Ticker / Trading Symbols using a Company Name as an input. You can use any NER which extracts Organizations / Companies / Parties to then send the output to this Entity Linking model and get the Ticker / Trading Symbol (given the company has one).
Predicted Entities
How to use
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
embeddings = nlp.BGEEmbeddings.pretrained("finance_bge_base_embeddings", "en", "finance/models")\
.setInputCols("ner_chunk") \
.setOutputCol("sentence_embeddings")
resolver = finance.SentenceEntityResolverModel.pretrained("finel_names2tickers_fe", "en", "finance/models") \
.setInputCols(["ner_chunk", "sentence_embeddings"]) \
.setOutputCol("name")\
.setDistanceFunction("EUCLIDEAN")
pipelineModel = nlp.Pipeline(
stages = [
documentAssembler,
embeddings,
resolver])
lp = LightPipeline(pipelineModel)
lp.fullAnnotate("Tesla")
Results
['TSLA']
Model Information
Model Name: | finel_names2tickers_fe |
Compatibility: | Finance NLP 1.0.0+ |
License: | Licensed |
Edition: | Official |
Input Labels: | [sentence_embeddings] |
Output Labels: | [normalized] |
Language: | en |
Size: | 115.6 MB |
Case sensitive: | false |
References
https://data.world/johnsnowlabs/list-of-companies-in-nasdaq-exchanges