Description
BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19.
How to use
...
embeddings = BertEmbeddings.pretrained("covidbert_large_uncased", "en") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"]))
...
val embeddings = BertEmbeddings.pretrained("covidbert_large_uncased", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
val data = Seq("I love NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["I love NLP"]
embeddings_df = nlu.load('en.embed.covidbert.large_uncased').predict(text, output_level='token')
embeddings_df
Results
en_embed_covidbert_large_uncased_embeddings token
[-1.934066891670227, 0.620597779750824, 0.0967... I
[-0.5530431866645813, 1.1948248147964478, -0.0... love
[0.255395770072937, 0.5808677077293396, 0.3073... NLP
Model Information
| Model Name: | covidbert_large_uncased |
| Type: | embeddings |
| Compatibility: | Spark NLP 2.6.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [word_embeddings] |
| Language: | [en] |
| Dimension: | 1024 |
| Case sensitive: | false |
Data Source
The model is imported from https://tfhub.dev/digitalepidemiologylab/covid-twitter-bert/2