Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

2_Dense/config.json

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+{
+    "in_features": 768,
+    "out_features": 3072,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

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3_Dense/config.json

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+{
+    "in_features": 3072,
+    "out_features": 768,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

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README.md

@@ -0,0 +1,533 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:15565
+- loss:MultipleNegativesRankingLoss
+base_model: google/embeddinggemma-300m
+widget:
+- source_sentence: I need to lock an object in my model so I can work on other parts
+    without accidentally selecting it. How can I do that?
+  sentences:
+  - You cannot use the following methods IsObjectLocked, LockObjects, UnlockObject,
+    SelectObject, SelectObjects, UnlockObjects, IsObjectSelectable, ShowObject, IsObjectNormal
+  - object
+  - "You can use the following methods to complete the task.\nmethod: LockObject\n\
+    description: Locks a single object. Locked objects are visible, and they can be\r\
+    \n    snapped to. But, they cannot be selected.\nsyntax: LockObject(object_id)\n\
+    parameters: object_id (guid): The identifier of an object\nreturns: bool: True\
+    \ or False indicating success or failure\n\nFollowing is the code that uses this\
+    \ method to complete the task as per user query.\n\n```python\nimport rhinoscriptsyntax\
+    \ as rs\n\n# Lock an object in the model to prevent accidental selection\nid =\
+    \ rs.GetObject(\"Select object to lock\")\nif id:\n    rs.LockObject(id)\n   \
+    \ print(\"Object locked successfully.\")\nelse:\n    print(\"No object selected.\"\
+    )\n```"
+- source_sentence: I want to create a cloud of points in my Rhino model. Can you show
+    me how to do that?
+  sentences:
+  - "You can use the following methods to complete the task.\nmethod: AddPointCloud\n\
+    description: Adds point cloud object to the document\nsyntax: AddPointCloud(points,\
+    \ colors=None)\nparameters: \npoints ([point, ....]): list of values where every\
+    \ multiple of three represents a point\r\ncolors ([color, ...]): list of colors\
+    \ to apply to each point\n\nreturns: \nguid: identifier of point cloud on success\n\
+    \n\nFollowing is the code that uses this method to complete the task as per user\
+    \ query.\n\n```python\nimport rhinoscriptsyntax as rs\n\n# Create a cloud of points\
+    \ in Rhino\npoints = [(0, 0, 0), (1, 1, 1), (2, 2, 2), (3, 3, 3)]  # Define points\n\
+    rs.AddPointCloud(points)  # Add the point cloud to the model\n```"
+  - geometry
+  - You cannot use the following methods PointCloudPoints, AddPoints, CreatePoint,
+    PointCloudCount, AddPoint, AddLine, PointCloudHidePoints, CreateVector, PointCoordinates
+- source_sentence: I need to find out which vertices make up each face of my mesh.
+    Can you help me with that?
+  sentences:
+  - "You can use the following methods to complete the task.\nmethod: MeshFaces\n\
+    description: Returns face vertices of a mesh\nsyntax: MeshFaces(object_id, face_type=True)\n\
+    parameters: object_id (guid): identifier of a mesh object\nface_type (bool, optional):\
+    \ The face type to be returned. True = both triangles and quads. False = only\
+    \ triangles\nreturns: list([point, point, point, point], ...): 3D points that\
+    \ define the face vertices of the mesh. If face_type is True, then faces are returned\
+    \ as both quads and triangles (4 3D points). For triangles, the third and fourth\
+    \ vertex will be identical. If face_type is False, then faces are returned as\
+    \ only triangles(3 3D points). Quads will be converted to triangles.\n\nFollowing\
+    \ is the code that uses this method to complete the task as per user query.\n\n\
+    ```python\nimport rhinoscriptsyntax as rs\n# Get the mesh object from the user\n\
+    obj = rs.GetObject(\"Select mesh\", rs.filter.mesh)\n# Retrieve the vertex indices\
+    \ for each face of the mesh\nfaces = rs.MeshFaces(obj, True)\nif faces:\n    rs.EnableRedraw(False)\n\
+    \    i = 0\n    while i < len(faces):\n        # Each face can be a triangle or\
+    \ a quad\n        face = faces[i:i+4] if len(faces) > i + 3 else faces[i:i+3]\n\
+    \        print(\"Face vertices:\", face)\n        i += 3 if len(face) == 3 else\
+    \ 4\n    rs.EnableRedraw(True)\n```"
+  - You cannot use the following methods MeshVertexFaces, MeshFaceVertices, MeshVertices,
+    MeshVertexCount, MeshFaceCenters, MeshTriangleCount, MeshQuadCount, MeshFaceCount,
+    MeshNakedEdgePoints
+  - mesh
+- source_sentence: Can you show me how to check if two transformation matrices are
+    the same in Rhino?
+  sentences:
+  - "You can use the following methods to complete the task.\nmethod: XformChangeBasis2\n\
+    description: Returns a change of basis transformation matrix of None on error\n\
+    syntax: XformChangeBasis2(x0,y0,z0,x1,y1,z1)\nparameters: \nx0,y0,z0 (vector):\
+    \ initial basis\r\nx1,y1,z1 (vector): final basis\n\nreturns: \ntransform: The\
+    \ 4x4 transformation matrix if successful\r\nNone: if not successful\n\n\nFollowing\
+    \ is the code that uses this method to complete the task as per user query.\n\n\
+    ```python\nimport rhinoscriptsyntax as rs\n\n# Function to check if two transformation\
+    \ matrices are the same\n# Parameters: mat1, mat2 - transformation matrices to\
+    \ compare\n# Returns: True if they are the same, False otherwise\ndef are_matrices_equal(mat1,\
+    \ mat2):\n    return rs.XformCompare(mat1, mat2) == 0\n\n# Example usage\nmatrix1\
+    \ = rs.XformChangeBasis2(1, 0, 0, 0, 1, 0)\nmatrix2 = rs.XformChangeBasis2(1,\
+    \ 0, 0, 0, 1, 0)\nresult = are_matrices_equal(matrix1, matrix2)\nprint(\"Matrices\
+    \ are equal:\" , result)\n```"
+  - You cannot use the following methods XformCompare, IsXformSimilarity, IsXformIdentity,
+    IsXformZero, CompareGeometry, CreateXform, VectorTransform, XformTranslation,
+    TransformObject, XformDeterminant
+  - transformation
+- source_sentence: I need to find where a flat surface meets a sphere. How can I do
+    that in Rhino?
+  sentences:
+  - plane
+  - You cannot use the following methods LineSphereIntersection, IsSphere, AddSphere,
+    LinePlaneIntersection, Angle, CircleCenterPoint, CurveCurveIntersection, CurveSurfaceIntersection,
+    AddCircle3Pt
+  - "You can use the following methods to complete the task.\nmethod: PlaneSphereIntersection\n\
+    description: Calculates the intersection of a plane and a sphere.\nsyntax: PlaneSphereIntersection(plane,\
+    \ sphere_plane, sphere_radius)\nparameters: plane (plane): The plane to intersect;\
+    \ sphere_plane (plane): Equatorial plane of the sphere (origin is center); sphere_radius\
+    \ (float): Radius of the sphere.\nreturns: list: [type, point/plane, radius] where\
+    \ type=0 for point, 1 for circle. None on error.\n\nFollowing is the code that\
+    \ uses this method to complete the task as per user query.\n\n```python\nimport\
+    \ rhinoscriptsyntax as rs\n\n# Define a flat surface as a plane\nplane = rs.WorldXYPlane()\n\
+    # Define the radius of the sphere\nradius = 10\n# Calculate the intersection between\
+    \ the plane and the sphere\nresults = rs.PlaneSphereIntersection(plane, plane,\
+    \ radius)\n\n# Check if there are results and handle them accordingly\nif results:\n\
+    \    if results[0] == 0:\n        # If the intersection is a point, add it to\
+    \ the document\n        rs.AddPoint(results[1])\n    else:\n        # If the intersection\
+    \ is a circle, add it to the document\n        rs.AddCircle(results[1], results[2])\n\
+    ```"
+datasets:
+- deebak14/embedding_tuple_data_v1
+- deebak14/embedding_triplet_data_v1
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on google/embeddinggemma-300m
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: base eval
+      type: base-eval
+    metrics:
+    - type: cosine_accuracy
+      value: 1.0
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on google/embeddinggemma-300m
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the [embedding_tuple_data_v1](https://huggingface.co/datasets/deebak14/embedding_tuple_data_v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
+- **Maximum Sequence Length:** 2048 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [embedding_tuple_data_v1](https://huggingface.co/datasets/deebak14/embedding_tuple_data_v1)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (4): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("deebak14/embedding_gemma_ft_v1")
+# Run inference
+queries = [
+    "I need to find where a flat surface meets a sphere. How can I do that in Rhino?",
+]
+documents = [
+    'You can use the following methods to complete the task.\nmethod: PlaneSphereIntersection\ndescription: Calculates the intersection of a plane and a sphere.\nsyntax: PlaneSphereIntersection(plane, sphere_plane, sphere_radius)\nparameters: plane (plane): The plane to intersect; sphere_plane (plane): Equatorial plane of the sphere (origin is center); sphere_radius (float): Radius of the sphere.\nreturns: list: [type, point/plane, radius] where type=0 for point, 1 for circle. None on error.\n\nFollowing is the code that uses this method to complete the task as per user query.\n\n```python\nimport rhinoscriptsyntax as rs\n\n# Define a flat surface as a plane\nplane = rs.WorldXYPlane()\n# Define the radius of the sphere\nradius = 10\n# Calculate the intersection between the plane and the sphere\nresults = rs.PlaneSphereIntersection(plane, plane, radius)\n\n# Check if there are results and handle them accordingly\nif results:\n    if results[0] == 0:\n        # If the intersection is a point, add it to the document\n        rs.AddPoint(results[1])\n    else:\n        # If the intersection is a circle, add it to the document\n        rs.AddCircle(results[1], results[2])\n```',
+    'You cannot use the following methods LineSphereIntersection, IsSphere, AddSphere, LinePlaneIntersection, Angle, CircleCenterPoint, CurveCurveIntersection, CurveSurfaceIntersection, AddCircle3Pt',
+    'plane',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 768] [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.6658,  0.4819, -0.1617]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Dataset: `base-eval`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value   |
+|:--------------------|:--------|
+| **cosine_accuracy** | **1.0** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### embedding_tuple_data_v1
+
+* Dataset: [embedding_tuple_data_v1](https://huggingface.co/datasets/deebak14/embedding_tuple_data_v1) at [b592a1a](https://huggingface.co/datasets/deebak14/embedding_tuple_data_v1/tree/b592a1af60cff995640f6979dbf36c01d38c40a8)
+* Size: 15,565 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 15.98 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 177.53 tokens</li><li>max: 810 tokens</li></ul> |
+* Samples:
+  | anchor                                                                         | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  |:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Provide an example of using AddRectangle.</code>                         | <code>import rhinoscriptsyntax as rs<br>plane = rs.WorldXYPlane()<br>plane = rs.RotatePlane(plane, 45.0, [0,0,1])<br>rs.AddRectangle(plane, 5.0, 15.0) <br><br>Metadata: <br>Name: AddRectangle<br>Category: curve<br>Function Signature: rs.AddRectangle(plane: plane, width: number, height: number) -> guid<br>Description: Add a rectangular curve to the document</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  | <code>How do I search for the total number of linetypes in my document?</code> | <code><br><br>You can use the following method:<br>Name: LinetypeCount<br>Category: linetype<br>Function Signature: rs.LinetypeCount() -> int<br>Description: Description: Returns the number of linetypes in the document.<br>Parameters:<br>    None<br>Returns:<br>    int: The number of linetypes in the document.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+  | <code>How do I maintain the shape of a curve while fitting it?</code>          | <code><br><br>You can use the following method:<br>Name: FitCurve<br>Category: curve<br>Function Signature: rs.FitCurve(curve_id: guid, degree: int = 3, distance_tolerance: float = -1, angle_tolerance: float = -1) -> guid<br>Description: Description: Reduces the number of control points of a curve while maintaining its general shape. This function is useful for replacing curves with many control points. For more information, see the Rhino help for the FitCrv command.<br>Parameters:<br>    curve_id (guid): Identifier of the curve object to be fitted.<br>    eg: '3D4F5A6B-7C8D-9E0F-1A2B-3C4D5E6F7A8B'<br>    degree (int, optional): The degree of the curve, which must be greater than 1. The default is 3.<br>    eg: 3<br>    distance_tolerance (float, optional): The fitting tolerance. If not specified or <= 0.0, the document absolute tolerance is used.<br>    eg: 0.01<br>    angle_tolerance (float, optional): The kink smoothing tolerance in degrees. If 0.0, all kinks are smoothed. If > 0.0, kinks smaller than this value are smoothed. If ...</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### embedding_triplet_data_v1
+
+* Dataset: [embedding_triplet_data_v1](https://huggingface.co/datasets/deebak14/embedding_triplet_data_v1) at [71ea1de](https://huggingface.co/datasets/deebak14/embedding_triplet_data_v1/tree/71ea1de1dd869a91bfa545c4f65e1d1d5ac41186)
+* Size: 476 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 476 samples:
+  |         | anchor                                                                             | positive                                                                               | negative                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                                 | string                                                                             |
+  | details | <ul><li>min: 15 tokens</li><li>mean: 23.13 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 111 tokens</li><li>mean: 252.44 tokens</li><li>max: 1015 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 42.48 tokens</li><li>max: 55 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                    | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | negative                                                                                                                                                                          |
+  |:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>I need to flatten a curved surface for laser cutting. How can I do that?</code>     | <code>You can use the following methods to complete the task.<br>method: UnrollSurface<br>description: Flattens a developable surface or polysurface<br>syntax: UnrollSurface(surface_id, explode=False, following_geometry=None, absolute_tolerance=None, relative_tolerance=None)<br>parameters: <br>surface_id (guid): the surface's identifier
  <br>explode (bool, optional): If True, the resulting surfaces ar not joined
  <br>following_geometry ({guid, ...]): List of curves, dots, and points which
  <br>  should be unrolled with the surface<br><br>returns: <br>list(guid, ...): of unrolled surface ids
  <br>tuple((guid, ...),(guid, ...)): if following_geometry is not None, a tuple
  <br>  [1] is the list of unrolled surface ids
  <br>  [2] is the list of unrolled following geometry<br><br><br>Following is the code that uses this method to complete the task as per user query.<br><br>```python<br>import rhinoscriptsyntax as rs<br><br># Flatten a curved surface for laser cutting<br>surface = rs.GetObject("Select curved surface to flatten", rs.filter.surface)<br>if surface:<br>    # Unrol...</code> | <code>You cannot use the following methods ConvertCurveToPolyline, MeshOutline, PullCurveToMesh, ExplodeText, MeshToNurb, IsCurvePlanar, Angle, AddFilletCurve, MeshVolume</code> |
+  | <code>Can you show me how to rotate a plane by 30 degrees around its normal axis?</code>  | <code>You can use the following methods to complete the task.<br>method: PlaneTransform<br>description: Transforms a plane.<br>syntax: PlaneTransform(plane, xform)<br>parameters: plane (plane): Plane to transform; xform (transform): Transformation to apply.<br>returns: plane: Resulting plane if successful, otherwise None.<br><br>Following is the code that uses this method to complete the task as per user query.<br><br>```python<br>import rhinoscriptsyntax as rs<br><br># Get the current construction plane<br>plane = rs.ViewCPlane()<br><br># Create a rotation transformation of 30 degrees around the normal axis (Z-axis)<br>xform = rs.XformRotation(30.0, plane.ZAxis, plane.Origin)<br><br># Apply the transformation to the plane<br>plane = rs.PlaneTransform(plane, xform)<br><br># Set the new construction plane<br>rs.ViewCPlane(None, plane)<br>```</code>                                                                                                                                                                                                                                         | <code>You cannot use the following methods RotatePlane, XformRotation1, PlaneFromNormal, VectorRotate, Angle, PlaneFromFrame, LinePlane, MovePlane, CreatePlane</code>            |
+  | <code>I want to change the height of a text dot I just created. How can I do that?</code> | <code>You can use the following methods to complete the task.<br>method: TextDotHeight<br>description: Returns or modified the font height of a text dot<br>syntax: TextDotHeight(object_id, height=None)<br>parameters: <br>object_id (guid): identifier of a text dot object
  <br>height (number, optional) new font height<br><br>returns: <br>number: If height is not specified, the current text dot height
  <br>number: If height is specified, the previous text dot height
  <br>None: on error<br><br><br>Following is the code that uses this method to complete the task as per user query.<br><br>```python<br>import rhinoscriptsyntax as rs<br># Change the height of a text dot<br>obj = rs.GetObject("Select text dot")<br>if rs.IsTextDot(obj):<br>    previous_height = rs.TextDotHeight(obj, 15.0)  # Set new height to 15.0<br>    print(f"Previous height was: {previous_height}")<br>```</code>                                                                                                                                                                                                                  | <code>You cannot use the following methods TextDotPoint, TextDotFont, TextDotText, TextObjectHeight, IsTextDot, AddTextDot, TextObjectFont, PointCoordinates, ExplodeText</code>  |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `prompts`: task: sentence similarity | query: 
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: task: sentence similarity | query: 
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | base-eval_cosine_accuracy |
+|:------:|:----:|:-------------:|:---------------:|:-------------------------:|
+| -1     | -1   | -             | -               | 0.0147                    |
+| 0.1028 | 100  | 0.1601        | -               | -                         |
+| 0.2055 | 200  | 0.0474        | 0.2296          | 0.8971                    |
+| 0.3083 | 300  | 0.0749        | -               | -                         |
+| 0.4111 | 400  | 0.1037        | 0.1457          | 0.9265                    |
+| 0.5139 | 500  | 0.0564        | -               | -                         |
+| 0.6166 | 600  | 0.0706        | 0.3362          | 0.9475                    |
+| 0.7194 | 700  | 0.0549        | -               | -                         |
+| 0.8222 | 800  | 0.0427        | 0.2154          | 0.9538                    |
+| 0.9250 | 900  | 0.0599        | -               | -                         |
+| 1.0277 | 1000 | 0.0656        | 0.2439          | 0.9706                    |
+| 1.1305 | 1100 | 0.0409        | -               | -                         |
+| 1.2333 | 1200 | 0.0283        | 0.2422          | 0.9727                    |
+| 1.3361 | 1300 | 0.0336        | -               | -                         |
+| 1.4388 | 1400 | 0.0338        | 0.2397          | 0.9664                    |
+| 1.5416 | 1500 | 0.0384        | -               | -                         |
+| 1.6444 | 1600 | 0.0271        | 0.1048          | 0.9832                    |
+| 1.7472 | 1700 | 0.0305        | -               | -                         |
+| 1.8499 | 1800 | 0.024         | 0.1172          | 0.9916                    |
+| 1.9527 | 1900 | 0.014         | -               | -                         |
+| 2.0555 | 2000 | 0.018         | 0.0898          | 0.9958                    |
+| 2.1583 | 2100 | 0.0091        | -               | -                         |
+| 2.2610 | 2200 | 0.0154        | 0.0721          | 0.9916                    |
+| 2.3638 | 2300 | 0.0123        | -               | -                         |
+| 2.4666 | 2400 | 0.0119        | 0.0876          | 0.9937                    |
+| 2.5694 | 2500 | 0.0173        | -               | -                         |
+| 2.6721 | 2600 | 0.0091        | 0.0482          | 1.0                       |
+| 2.7749 | 2700 | 0.0211        | -               | -                         |
+| 2.8777 | 2800 | 0.0146        | 0.0550          | 1.0                       |
+| 2.9805 | 2900 | 0.0101        | -               | -                         |
+| -1     | -1   | -             | -               | 1.0                       |
+
+
+### Framework Versions
+- Python: 3.12.11
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.1
+- PyTorch: 2.8.0+cu126
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3TextModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "dtype": "float32",
+  "eos_token_id": 1,
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+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_types": [
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+    "sliding_attention",
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+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 2048,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 3,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
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+  "sliding_window": 512,
+  "transformers_version": "4.56.1",
+  "use_bidirectional_attention": true,
+  "use_cache": true,
+  "vocab_size": 262144
+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.1",
+    "pytorch": "2.8.0+cu126"
+  },
+  "prompts": {
+    "query": "task: search result | query: ",
+    "document": "title: none | text: ",
+    "BitextMining": "task: search result | query: ",
+    "Clustering": "task: clustering | query: ",
+    "Classification": "task: classification | query: ",
+    "InstructionRetrieval": "task: code retrieval | query: ",
+    "MultilabelClassification": "task: classification | query: ",
+    "PairClassification": "task: sentence similarity | query: ",
+    "Reranking": "task: search result | query: ",
+    "Retrieval": "task: search result | query: ",
+    "Retrieval-query": "task: search result | query: ",
+    "Retrieval-document": "title: none | text: ",
+    "STS": "task: sentence similarity | query: ",
+    "Summarization": "task: summarization | query: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 1211486072

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 4,
+    "name": "4",
+    "path": "4_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 2048,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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