2023-06-21-bert_embeddings_distil_clinical_en (#13861)

* Add model 2023-06-21-bert_embeddings_distil_clinical_en * Add model 2023-06-21-bert_embeddings_carlbert_webex_mlm_spatial_en * Add model 2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c2_en * Add model 2023-06-21-bert_embeddings_lsg16k_Italian_Legal_it * Add model 2023-06-21-bert_embeddings_chemical_uncased_finetuned_cust_c1_cust_en * Add model 2023-06-21-bert_embeddings_legalbert_adept_en * Add model 2023-06-21-bert_embeddings_base_uncased_issues_128_en * Add model 2023-06-21-bert_embeddings_pretrain_ko * Add model 2023-06-21-bert_embeddings_olm_base_uncased_oct_2022_en * Add model 2023-06-21-legalectra_small_es * Add model 2023-06-21-biobert_pubmed_base_cased_v1.2_en * Add model 2023-06-21-bert_embeddings_jobbert_base_cased_en * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_700000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_800000_cased_generator_de * Add model 2023-06-21-legalectra_base_es * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_900000_cased_generator_de * Add model 2023-06-21-bert_embeddings_scibert_scivocab_finetuned_cord19_en * Add model 2023-06-21-bert_embeddings_InLegalBERT_en * Add model 2023-06-21-bert_embeddings_InCaseLawBERT_en * Add model 2023-06-21-bert_base_uncased_contracts_en * Add model 2023-06-21-electra_embeddings_electra_base_turkish_mc4_uncased_generator_tr * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_500000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_generator_en * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_200000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_italian_xxl_cased_generator_it * Add model 2023-06-21-bert_embeddings_bioclinicalbert_finetuned_covid_papers_en * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_1000000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de * Add model 2023-06-21-electra_embeddings_finance_koelectra_base_generator_ko * Add model 2023-06-21-electra_embeddings_koelectra_base_v2_generator_ko * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_300000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_turkish_mc4_cased_generator_tr * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_small_generator_en * Add model 2023-06-21-electra_embeddings_electra_large_generator_en * Add model 2023-06-21-electra_embeddings_electricidad_base_generator_es * Add model 2023-06-21-electra_embeddings_gelectra_large_generator_de * Add model 2023-06-21-electra_embeddings_koelectra_base_generator_ko * Add model 2023-06-21-electra_embeddings_koelectra_base_v3_generator_ko * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_0_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_100000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_400000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_base_gc4_64k_600000_cased_generator_de * Add model 2023-06-21-electra_embeddings_electra_tagalog_small_cased_generator_tl * Add model 2023-06-21-electra_embeddings_gelectra_base_generator_de * Add model 2023-06-21-electra_embeddings_electra_tagalog_base_cased_generator_tl * Add model 2023-06-21-bert_sentence_embeddings_financial_de * Add model 2023-06-21-electra_embeddings_electra_small_japanese_generator_ja * Add model 2023-06-21-electra_embeddings_electra_tagalog_base_uncased_generator_tl * Add model 2023-06-21-electra_embeddings_koelectra_small_generator_ko * Add model 2023-06-21-electra_embeddings_finance_koelectra_small_generator_ko * Add model 2023-06-21-bert_embeddings_sec_bert_base_en * Add model 2023-06-21-electra_embeddings_kr_electra_generator_ko * Add model 2023-06-21-bert_embeddings_sec_bert_sh_en * Add model 2023-06-21-bert_embeddings_german_financial_statements_bert_de * Add model 2023-06-21-electra_embeddings_electra_tagalog_small_uncased_generator_tl * Add model 2023-06-21-bert_embeddings_javanese_bert_small_jv * Add model 2023-06-21-bert_embeddings_finest_bert_en * Add model 2023-06-21-bert_embeddings_indic_transformers_te_bert_te * Add model 2023-06-21-bert_embeddings_gbert_base_de * Add model 2023-06-21-bert_embeddings_indic_transformers_hi_bert_hi * Add model 2023-06-21-bert_embeddings_hateBERT_en * Add model 2023-06-21-bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1_en * Add model 2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en * Add model 2023-06-21-bert_embeddings_indic_transformers_te_bert_te * Add model 2023-06-21-bert_embeddings_hseBert_it_cased_it * Add model 2023-06-21-bert_embeddings_finbert_pretrain_yiyanghkust_en * Add model 2023-06-21-bert_embeddings_dpr_spanish_question_encoder_allqa_base_es * Add model 2023-06-21-bert_embeddings_dziribert_ar * Add model 2023-06-21-bert_embeddings_deberta_base_uncased_en * Add model 2023-06-21-bert_embeddings_dbert_ko * Add model 2023-06-21-bert_embeddings_javanese_bert_small_imdb_jv * Add model 2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_squades_base_es * Add model 2023-06-21-bert_embeddings_dpr_spanish_question_encoder_squades_base_es * Add model 2023-06-21-bert_embeddings_crosloengual_bert_en * Add model 2023-06-21-bert_embeddings_clinical_pubmed_bert_base_512_en * Add model 2023-06-21-bert_embeddings_dpr_spanish_passage_encoder_allqa_base_es * Add model 2023-06-21-bert_embeddings_legal_bert_base_uncased_en * Add model 2023-06-21-biobert_embeddings_all_pt * Add model 2023-06-21-bert_embeddings_wineberto_italian_cased_it * Add model 2023-06-21-bert_embeddings_clinical_pubmed_bert_base_128_en * Add model 2023-06-21-biobert_embeddings_clinical_pt * Add model 2023-06-21-bert_embeddings_telugu_bertu_te * Add model 2023-06-21-bert_embeddings_wobert_chinese_plus_zh * Add model 2023-06-21-bert_embeddings_wineberto_italian_cased_it * Add model 2023-06-21-bert_embeddings_sikuroberta_zh * Add model 2023-06-21-biobert_embeddings_biomedical_pt * Add model 2023-06-21-bert_embeddings_sikubert_zh * Add model 2023-06-21-bert_embeddings_psych_search_en * Add model 2023-06-21-bert_embeddings_marathi_bert_mr * Add model 2023-06-21-bert_embeddings_netbert_en * Add model 2023-06-21-bert_embeddings_mbert_ar_c19_ar * Add model 2023-06-21-bert_embeddings_multi_dialect_bert_base_arabic_ar * Add model 2023-06-21-bert_embeddings_lic_class_scancode_bert_base_cased_L32_1_en * Add model 2023-06-21-bert_embeddings_MARBERTv2_ar * Add model 2023-06-21-bert_embeddings_bert_base_cased_pt_lenerbr_pt * Add model 2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_half_ar * Add model 2023-06-21-bert_embeddings_bert_base_german_cased_oldvocab_de * Add model 2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_ar * Add model 2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_eighth_ar * Add model 2023-06-21-bert_embeddings_bert_base_german_uncased_de * Add model 2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_quarter_ar * Add model 2023-06-21-bert_embeddings_bert_base_historical_german_rw_cased_de * Add model 2023-06-21-bert_embeddings_bert_base_italian_xxl_uncased_it * Add model 2023-06-21-bert_embeddings_bert_base_arabertv2_ar * Add model 2023-06-21-bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth_ar * Add model 2023-06-21-bert_embeddings_bert_base_arabic_camelbert_mix_ar * Add model 2023-06-21-bert_embeddings_bert_base_italian_xxl_cased_it * Add model 2023-06-21-bert_embeddings_bert_base_gl_cased_pt * Add model 2023-06-21-bert_embeddings_MARBERT_ar * Add model 2023-06-21-bert_embeddings_AraBertMo_base_V1_ar * Add model 2023-06-21-bert_embeddings_bert_base_arabic_ar * Add model 2023-06-21-bert_embeddings_DarijaBERT_ar * Add model 2023-06-21-bert_embeddings_Ara_DialectBERT_ar * Add model 2023-06-21-bert_embeddings_German_MedBERT_de * Add model 2023-06-21-bert_embeddings_bert_base_arabertv02_twitter_ar * Add model 2023-06-21-bert_embeddings_FinancialBERT_en * Add model 2023-06-21-bert_embeddings_ARBERT_ar * Add model 2023-06-21-bert_embeddings_COVID_SciBERT_en * Add model 2023-06-21-bert_embeddings_alberti_bert_base_multilingual_cased_es * Add model 2023-06-21-bert_embeddings_agriculture_bert_uncased_en * Add model 2023-06-21-bert_embeddings_bangla_bert_bn * Add model 2023-06-21-bert_embeddings_bert_kor_base_ko * Add model 2023-06-21-bert_embeddings_bert_base_arabertv02_ar * Add model 2023-06-21-bert_embeddings_arabert_c19_ar * Add model 2023-06-21-bert_embeddings_bert_base_5lang_cased_es * Add model 2023-06-21-bert_embeddings_bert_base_arabertv01_ar * Add model 2023-06-21-bert_embeddings_bangla_bert_base_bn * Add model 2023-06-21-bert_embeddings_bert_medium_arabic_ar * Add model 2023-06-21-bert_embeddings_bert_political_election2020_twitter_mlm_en * Add model 2023-06-21-bert_embeddings_bert_mini_arabic_ar * Add model 2023-06-21-bert_embeddings_bert_base_arabert_ar * Add model 2023-06-21-bert_embeddings_beto_gn_base_cased_es * Add model 2023-06-21-bert_embeddings_chemical_bert_uncased_en * Add model 2023-06-21-bert_embeddings_bert_base_ko * Add model 2023-06-21-bert_embeddings_chefberto_italian_cased_it * Add model 2023-06-21-bert_embeddings_childes_bert_en * Add model 2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes_pt * Add model 2023-06-21-bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos_pt * Add model 2023-06-21-bert_embeddings_bert_base_portuguese_cased_pt * Add model 2023-06-21-bert_embeddings_bert_base_qarib60_1790k_ar * Add model 2023-06-21-bert_embeddings_bert_base_uncased_dstc9_en * Add model 2023-06-21-bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier_en * Add model 2023-06-21-bert_embeddings_bert_base_qarib_ar * Add model 2023-06-21-bert_embeddings_bert_base_uncased_sparse_70_unstructured_en * Add model 2023-06-21-ms_bluebert_base_uncased_en * Add model 2023-06-21-bert_embeddings_bert_base_qarib60_860k_ar * fixing wrong spark version and removing tensorflow --------- Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com> Co-authored-by: MaziyarPanahi <maziyar.panahi@iscpif.fr>
JohnSnowLabs · Jun 21, 2023 · d054074 · d054074
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diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_base_uncased_contracts_en.md
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+---
+layout: model
+title: English Legal Contracts BertEmbeddings model (Base, Uncased)
+author: John Snow Labs
+name: bert_base_uncased_contracts
+date: 2023-06-21
+tags: [open_source, bert, embeddings, finance, contracts, en, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained Word Embeddings model, trained on legal contracts, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-uncased-contracts` is a English model originally trained by `nlpaueb`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_base_uncased_contracts_en_5.0.0_3.0_1687337099443.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_uncased_contracts_en_5.0.0_3.0_1687337099443.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+
+embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","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)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","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)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.bert.contracts.uncased_base").predict("""I love Spark NLP.""")
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+
+embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","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)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = BertEmbeddings.pretrained("bert_base_uncased_contracts","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)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.bert.contracts.uncased_base").predict("""I love Spark NLP.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_base_uncased_contracts|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|en|
+|Size:|407.1 MB|
+|Case sensitive:|true|
diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md b/docs/_posts/ahmedlone127/2023-06-21-bert_embeddings_ARBERT_ar.md
@@ -0,0 +1,149 @@
+---
+layout: model
+title: Arabic Bert Embeddings (ARBERT model)
+author: John Snow Labs
+name: bert_embeddings_ARBERT
+date: 2023-06-21
+tags: [bert, embeddings, ar, open_source, onnx]
+task: Embeddings
+language: ar
+edition: Spark NLP 5.0.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `ARBERT` is a Arabic model orginally trained by `UBC-NLP`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/bert_embeddings_ARBERT_ar_5.0.0_3.0_1687368387135.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_embeddings_ARBERT_ar_5.0.0_3.0_1687368387135.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("أنا أحب شرارة NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ar.embed.arbert").predict("""أنا أحب شرارة NLP""")
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = BertEmbeddings.pretrained("bert_embeddings_ARBERT","ar") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("أنا أحب شرارة NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ar.embed.arbert").predict("""أنا أحب شرارة NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_embeddings_ARBERT|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ar|
+|Size:|605.3 MB|
+|Case sensitive:|true|