Description
The word embedding models were based on Word2Vec, trained on a mix of different datasets. We used public data and in-house annotated documents.
Predicted Entities
How to use
model = nlp.WordEmbeddingsModel.pretrained("legal_word_embeddings","en","legal/models")\
.setInputCols(["sentence","token"])\
.setOutputCol("embeddings")
Model Information
| Model Name: | legal_word_embeddings |
| Type: | embeddings |
| Compatibility: | Legal NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [word_embeddings] |
| Language: | en |
| Size: | 84.9 MB |
| Case sensitive: | false |
| Dimension: | 200 |
References
Public data and in-house annotated documents