Pretrained Models
pretrained(name, lang) function to use
English
| Model | Name | en |
|---|---|---|
| LemmatizerModel (Lemmatizer) | lemma_antbnc |
Download |
| PerceptronModel (POS) | pos_anc |
Download |
| NerCRFModel (NER with GloVe) | ner_crf |
Download |
| NerDLModel (NER with GloVe) | ner_dl |
Download |
| NerDLModel (NER with GloVe) | ner_dl_contrib |
Download |
| NerDLModel (NER with BERT) | ner_dl_bert_base_cased |
Download |
| NerDLModel (OntoNotes with GloVe 100d) | onto_100 |
Download |
| NerDLModel (OntoNotes with GloVe 300d) | onto_300 |
Download |
| WordEmbeddings (GloVe) | glove_100d |
Download |
| BertEmbeddings (base_uncased) | bert_base_uncased |
Download |
| BertEmbeddings (base_cased) | bert_base_cased |
Download |
| BertEmbeddings (large_uncased) | bert_large_uncased |
Download |
| BertEmbeddings (large_cased) | bert_large_cased |
Download |
| DeepSentenceDetector | ner_dl_sentence |
Download |
| ContextSpellCheckerModel (Spell Checker) | spellcheck_dl |
Download |
| SymmetricDeleteModel (Spell Checker) | spellcheck_sd |
Download |
| NorvigSweetingModel (Spell Checker) | spellcheck_norvig |
Download |
| ViveknSentimentModel (Sentiment) | sentiment_vivekn |
Download |
| DependencyParser (Dependency) | dependency_conllu |
Download |
| TypedDependencyParser (Dependency) | dependency_typed_conllu |
Download |
French
| Model | Name | fr |
|---|---|---|
| LemmatizerModel (Lemmatizer) | lemma |
Download |
| PerceptronModel (POS UD) | pos_ud_gsd |
Download |
| NerDLModel (glove_840B_300) | wikiner_840B_300 |
Download |
| Feature | Description |
|---|---|
| Lemma | Trained by Lemmatizer annotator on lemmatization-lists by Michal Měchura |
| POS | Trained by PerceptronApproach annotator on the Universal Dependencies |
| NER | Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the WikiNER corpus and supports the identification of PER, LOC, ORG and MISC entities |
German
| Model | Name | de |
|---|---|---|
| LemmatizerModel (Lemmatizer) | lemma |
de |
| PerceptronModel (POS UD) | pos_ud_hdt |
de |
| NerDLModel (glove_840B_300) | wikiner_840B_300 |
de |
| Feature | Description |
|---|---|
| Lemma | Trained by Lemmatizer annotator on lemmatization-lists by Michal Měchura |
| POS | Trained by PerceptronApproach annotator on the Universal Dependencies |
| NER | Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the WikiNER corpus and supports the identification of PER, LOC, ORG and MISC entities |
Italian
| Model | Name | it |
|---|---|---|
| LemmatizerModel (Lemmatizer) | lemma_dxc |
Download |
| SentimentDetector (Sentiment) | sentiment_dxc |
Download |
| PerceptronModel (POS UD) | pos_ud_isdt |
Download |
| NerDLModel (glove_840B_300) | wikiner_840B_300 |
Download |
| Feature | Description |
|---|---|
| Lemma | Trained by Lemmatizer annotator on DXC Technology dataset |
| POS | Trained by PerceptronApproach annotator on the Universal Dependencies |
| NER | Trained by NerDLApproach annotator with Char CNNs - BiLSTM - CRF and GloVe Embeddings on the WikiNER corpus and supports the identification of PER, LOC, ORG and MISC entities |
Multi-language
| Model | Name | xx |
|---|---|---|
| WordEmbeddings (GloVe) | glove_840B_300 |
Download |
| WordEmbeddings (GloVe) | glove_6B_300 |
Download |
| BertEmbeddings (multi_cased) | bert_multi_cased |
Download |
How to use Pretrained Models
Online
You can follow this approach to use Spark NLP pretrained models:
# load NER model trained by deep learning approach and GloVe word embeddings
ner_dl = NerDLModel.pretrained('ner_dl')
# load NER model trained by deep learning approach and BERT word embeddings
ner_bert = NerDLModel.pretrained('ner_dl_bert')
The default language is en, so for other laguages you should set the language:
// load French POS tagger model trained by Universal Dependencies
val french_pos = PerceptronModel.pretrained("pos_ud_gsd", lang="fr")
// load Italain LemmatizerModel
val italian_lemma = LemmatizerModel.pretrained("lemma_dxc", lang="it")
Offline
If you have any trouble using online pipelines or models in your environment (maybe it’s air-gapped), you can directly download them for offline use.
After downloading offline models/pipelines and extracting them, here is how you can use them iside your code (the path could be a shared storage like HDFS in a cluster):
- Loading
PerceptronModelannotator model inside Spark NLP Pipeline
val french_pos = PerceptronModel.load("/tmp/pos_ud_gsd_fr_2.0.2_2.4_1556531457346/")
.setInputCols("document", "token")
.setOutputCol("pos")