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Models hub #13913

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merged 39 commits into from
Aug 2, 2023
Merged

Models hub #13913

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57d855e
Merge branch 'master' into models_hub
maziyarpanahi Nov 21, 2022
41cda2d
Merge branch 'models_hub' of https://github.com/JohnSnowLabs/spark-nl…
maziyarpanahi Nov 25, 2022
6c39602
Merge branch 'master' into models_hub
maziyarpanahi Dec 15, 2022
bed4adb
Merge branch 'master' into models_hub
maziyarpanahi Dec 21, 2022
cf0b08f
Merge branch 'master' into models_hub
maziyarpanahi Feb 7, 2023
93d6753
Merge branch 'master' into models_hub
maziyarpanahi Mar 14, 2023
afb700e
Add model 2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en (#1…
jsl-models Apr 13, 2023
bb9a155
2023-04-20-distilbert_base_uncased_mnli_en (#13761)
jsl-models Apr 20, 2023
ea0ba05
2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinl…
jsl-models Apr 21, 2023
9afffb1
2023-05-04-roberta_base_zero_shot_classifier_nli_en (#13781)
jsl-models May 4, 2023
f4356e5
2023-05-09-distilbart_xsum_6_6_en (#13788)
jsl-models May 10, 2023
04149fb
Merge branch 'master' into models_hub
maziyarpanahi May 10, 2023
de3e19e
2023-05-11-distilbart_cnn_12_6_en (#13795)
jsl-models May 11, 2023
71de0f7
2023-05-19-match_pattern_en (#13805)
jsl-models May 21, 2023
f28ea8e
2023-05-22-explain_document_md_fr (#13811)
jsl-models May 23, 2023
4049881
2023-05-24-explain_document_md_fr (#13821)
jsl-models May 25, 2023
e4e465e
Add model 2023-05-25-explain_document_md_fr (#13827)
jsl-models May 25, 2023
e8e01a5
2023-05-25-dependency_parse_en (#13828)
jsl-models May 26, 2023
9c0a24e
Merge branch 'master' into models_hub
maziyarpanahi May 26, 2023
2fd64c3
2023-05-25-distilcamembert_french_legal_fr (#13826)
jsl-models May 26, 2023
795ebf8
Update title for 2023-05-25-distilcamembert_french_legal_fr.md (#13831)
Mary-Sci May 26, 2023
c04ca51
2023-05-27-explain_document_md_fr (#13836)
jsl-models May 27, 2023
4d64d1b
2023-05-28-longformer_base_english_legal_en (#13838)
jsl-models May 28, 2023
02a9afb
2023-05-28-xlm_longformer_base_english_legal_en (#13839)
jsl-models May 29, 2023
d054074
2023-06-21-bert_embeddings_distil_clinical_en (#13861)
jsl-models Jun 21, 2023
43ab794
2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en …
jsl-models Jun 26, 2023
7cde44f
2023-06-27-roberta_embeddings_robertinh_gl (#13868)
jsl-models Jun 27, 2023
ced98b6
Add model 2023-06-29-xlmroberta_embeddings_paraphrase_mpnet_base_v2_x…
jsl-models Jun 30, 2023
dfaabd4
2023-06-08-instructor_base_en (#13850)
jsl-models Jul 1, 2023
59113cd
2023-06-28-roberta_base_en (#13871)
jsl-models Jul 1, 2023
740f4fb
Merge branch 'master' into models_hub
maziyarpanahi Jul 3, 2023
c999bd6
Merge branch 'master' into models_hub
maziyarpanahi Jul 4, 2023
27840ed
Add model 2023-07-05-image_classifier_convnext_tiny_224_local_en (#13…
jsl-models Jul 5, 2023
566b6ee
Add model 2023-07-06-quora_distilbert_multilingual_en (#13882)
jsl-models Jul 18, 2023
d246455
removed duplicated sections (#13885)
ahmedlone127 Jul 18, 2023
182bc05
Add model 2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli…
jsl-models Jul 21, 2023
9a1bea5
Add model 2023-07-28-twitter_xlm_roberta_base_sentiment_en (#13905)
jsl-models Jul 28, 2023
cc00383
2023-07-30-albert_embeddings_ALR_BERT_ro (#13910)
jsl-models Aug 2, 2023
b6d3cf1
2023-07-28-twitter_xlm_roberta_base_sentiment_en (#13906)
jsl-models Aug 2, 2023
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9 changes: 0 additions & 9 deletions docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md
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Expand Up @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
result = pipeline.annotate("""I love johnsnowlabs! """)
```

</div>

{:.model-param}

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32 changes: 0 additions & 32 deletions docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md
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Expand Up @@ -34,39 +34,7 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}

```python

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en')

result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")


```
```scala

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en")

val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")

```

{:.nlu-block}
```python

import nlu
text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""]
result_df = nlu.load('en.classify').predict(text)
result_df

```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
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26 changes: 0 additions & 26 deletions docs/_posts/ahmedlone127/2023-05-20-clean_pattern_en.md
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Expand Up @@ -34,33 +34,7 @@ The clean_pattern is a pretrained pipeline that we can use to process text with

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python


from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('clean_pattern', lang = 'en')
annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
annotations.keys()
```
```scala


val pipeline = new PretrainedPipeline("clean_pattern", lang = "en")
val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
```

{:.nlu-block}
```python


import nlu
text = [""Hello from John Snow Labs ! ""]
result_df = nlu.load('en.clean.pattern').predict(text)
result_df
```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
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27 changes: 0 additions & 27 deletions docs/_posts/ahmedlone127/2023-05-20-clean_stop_en.md
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Expand Up @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('clean_stop', lang = 'en')
annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
annotations.keys()

```
```scala

val pipeline = new PretrainedPipeline("clean_stop", lang = "en")
val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)


```

{:.nlu-block}
```python

import nlu
text = [""Hello from John Snow Labs ! ""]
result_df = nlu.load('en.clean.stop').predict(text)
result_df

```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
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27 changes: 0 additions & 27 deletions docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_lg_fr.md
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Expand Up @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('entity_recognizer_lg', lang = 'fr')
annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
annotations.keys()

```
```scala

val pipeline = new PretrainedPipeline("entity_recognizer_lg", lang = "fr")
val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)


```

{:.nlu-block}
```python

import nlu
text = [""Bonjour de John Snow Labs! ""]
result_df = nlu.load('fr.ner').predict(text)
result_df

```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
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27 changes: 0 additions & 27 deletions docs/_posts/ahmedlone127/2023-05-20-entity_recognizer_md_fr.md
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Expand Up @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('entity_recognizer_md', lang = 'fr')
annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
annotations.keys()

```
```scala

val pipeline = new PretrainedPipeline("entity_recognizer_md", lang = "fr")
val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)


```

{:.nlu-block}
```python

import nlu
text = [""Bonjour de John Snow Labs! ""]
result_df = nlu.load('fr.ner.md').predict(text)
result_df

```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
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27 changes: 0 additions & 27 deletions docs/_posts/ahmedlone127/2023-05-20-explain_document_dl_en.md
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Expand Up @@ -34,35 +34,8 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline('explain_document_dl', lang = 'en')
annotations = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")[0]
annotations.keys()

```
```scala

val pipeline = new PretrainedPipeline("explain_document_dl", lang = "en")
val result = pipeline.fullAnnotate("The Mona Lisa is an oil painting from the 16th century.")(0)


```

{:.nlu-block}
```python

import nlu
text = ["The Mona Lisa is an oil painting from the 16th century."]
result_df = nlu.load('en.explain.dl').predict(text)
result_df

```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
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27 changes: 0 additions & 27 deletions docs/_posts/ahmedlone127/2023-05-20-explain_document_md_de.md
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Expand Up @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('explain_document_md', lang = 'de')
annotations = pipeline.fullAnnotate(""Hallo aus John Snow Labs! "")[0]
annotations.keys()

```
```scala

val pipeline = new PretrainedPipeline("explain_document_md", lang = "de")
val result = pipeline.fullAnnotate("Hallo aus John Snow Labs! ")(0)


```

{:.nlu-block}
```python

import nlu
text = [""Hallo aus John Snow Labs! ""]
result_df = nlu.load('de.explain.document').predict(text)
result_df

```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
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27 changes: 0 additions & 27 deletions docs/_posts/ahmedlone127/2023-05-20-explain_document_md_fr.md
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Original file line number Diff line number Diff line change
Expand Up @@ -33,35 +33,8 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('explain_document_md', lang = 'fr')
annotations = pipeline.fullAnnotate(""Bonjour de John Snow Labs! "")[0]
annotations.keys()

```
```scala

val pipeline = new PretrainedPipeline("explain_document_md", lang = "fr")
val result = pipeline.fullAnnotate("Bonjour de John Snow Labs! ")(0)


```

{:.nlu-block}
```python

import nlu
text = [""Bonjour de John Snow Labs! ""]
result_df = nlu.load('fr.explain.md').predict(text)
result_df

```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
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26 changes: 0 additions & 26 deletions docs/_posts/ahmedlone127/2023-05-20-explain_document_ml_en.md
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Original file line number Diff line number Diff line change
Expand Up @@ -34,33 +34,7 @@ It performs most of the common text processing tasks on your dataframe

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python


from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline('explain_document_ml', lang = 'en')
annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
annotations.keys()
```
```scala


val pipeline = new PretrainedPipeline("explain_document_ml", lang = "en")
val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
```

{:.nlu-block}
```python


import nlu
text = [""Hello from John Snow Labs ! ""]
result_df = nlu.load('en.explain').predict(text)
result_df
```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
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28 changes: 0 additions & 28 deletions docs/_posts/ahmedlone127/2023-05-20-match_datetime_en.md
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Expand Up @@ -32,35 +32,7 @@ DateMatcher based on yyyy/MM/dd

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

pipeline_local = PretrainedPipeline('match_datetime')

tres = pipeline_local.fullAnnotate(input_list)[0]
for dte in tres['date']:
sent = tres['sentence'][int(dte.metadata['sentence'])]
print (f'text/chunk {sent.result[dte.begin:dte.end+1]} | mapped_date: {dte.result}')
```
```scala

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP

SparkNLP.version()

val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family.
He also visited and the day before, but at that time he was alone.
David again visited today with his colleagues.
He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text")

val pipeline = PretrainedPipeline("match_datetime", lang="en")

val annotation = pipeline.transform(testData)

annotation.show()
```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
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9 changes: 0 additions & 9 deletions docs/_posts/ahmedlone127/2023-05-20-match_pattern_en.md
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -33,16 +33,7 @@ The match_pattern is a pretrained pipeline that we can use to process text with

## How to use

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python

from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
result = pipeline.annotate("""I love johnsnowlabs! """)
```

</div>

{:.model-param}

Expand Down
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