Add model 2023-12-07-finembeddings_bge_base_en (#812)

Co-authored-by: dcecchini <dadachini@hotmail.com>
JohnSnowLabs · Dec 19, 2023 · c3d98fa · c3d98fa
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+---
+layout: model
+title: Finance Embeddings BGE Base
+author: John Snow Labs
+name: finembeddings_bge_base
+date: 2023-12-07
+tags: [finance, en, licensed, bge, embeddings, onnx]
+task: Embeddings
+language: en
+edition: Finance NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is a legal version of the BGE base model fine-tuned on in-house curated datasets. Reference: Xiao, S., Liu, Z., Zhang, P., & Muennighof, N. (2023). C-pack: Packaged resources to advance general chinese embedding. arXiv preprint arXiv:2309.07597.
+
+## 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/finance/models/finembeddings_bge_base_en_1.0.0_3.0_1701948521741.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/finance/models/finembeddings_bge_base_en_1.0.0_3.0_1701948521741.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 = nlp.DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = nlp.Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+
+bge = nlp.BertEmbeddings.pretrained("finembeddings_bge_base", "en", "finance/models")\
+    .setInputCols(["document", "token"])\
+    .setOutputCol("bge")
+
+pipeline = nlp.Pipeline(
+    stages = [
+        documentAssembler,
+        tokenizer,
+        bge
+  ])
+
+data = spark.createDataFrame([['
+    ''What is the best way to invest in the stock market?'''
+]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+.selectExpr("explode(bge.embeddings) as bge_embeddings").show(truncate=100)
+```
+
+</div>
+
+## Results
+
+```bash
++----------------------------------------------------------------------------------------------------+
+|                                                                                      bge_embeddings|
++----------------------------------------------------------------------------------------------------+
+|[0.70071065, 0.8154926, 0.3667199, 0.49541458, 0.5675478, 0.47981235, 0.09903594, 1.0118086, -0.3...|
+|[0.5844246, 0.897823, 0.36319774, 0.33672202, 0.6926622, 0.62645215, 0.21583402, 0.99781555, -0.0...|
+|[0.5678047, 0.9290247, 0.19549623, 0.29991657, 0.6558282, 0.60267514, 0.2365676, 0.87947553, -0.1...|
+|[0.31799358, 0.60279167, 0.7648379, 0.2832115, 0.45711696, 0.12192034, -0.10309678, 1.1410849, -0...|
+|[1.0170714, 1.1024956, 0.59346, 0.4784618, 0.81034416, 0.2503267, -0.02142908, 0.6190611, -0.1401...|
+|[0.8248961, 1.1220868, 0.27929437, 0.20173876, 0.6809691, 0.6311508, 0.15206291, 0.8089775, 0.317...|
+|[0.76785743, 0.9963818, 0.21050292, 0.2416854, 1.0152707, 0.18767616, 0.27576423, 0.85077125, 0.3...|
+|[0.654324, 1.1681782, 0.17568657, 0.23243408, 0.76372075, 0.6539263, 0.2841307, 1.224574, 0.21359...|
+|[0.5922923, 1.2471354, 0.090304464, 0.48645073, 0.59852546, 0.8716394, 0.34509993, 0.9442089, 0.1...|
+|[0.72195786, 0.9363174, 0.06630206, 0.27642763, 0.7145356, 0.23325293, 0.12738094, 1.0298125, -0....|
+|[0.45599157, 0.9871535, 0.15671916, 0.17181304, 0.93662477, 0.27518728, -0.18060194, 0.93082047, ...|
+|[0.6865296, 1.052128, 0.2681757, 0.32934788, 0.47195143, 0.81678694, 0.012849957, 1.0271766, -0.0...|
++----------------------------------------------------------------------------------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|finembeddings_bge_base|
+|Compatibility:|Finance NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bge_embeddings]|
+|Language:|en|
+|Size:|397.2 MB|
+|Case sensitive:|false|
+
+## References
+
+In-house curated financial datasets.