Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,854 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:882
+- loss:MatryoshkaLoss
+- loss:TripletLoss
+base_model: BAAI/bge-base-en-v1.5
+datasets: []
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+widget:
+- source_sentence: 'hide: footer
+
+
+    Fields
+
+
+    Fields in Argilla are define the content of a record that will be reviewed by
+    a user.'
+  sentences:
+  - The tourists tried to hide their footprints in the sand as they walked along the
+    deserted beach.
+  - Can the rg.Suggestion class be used to handle model predictions in Argilla?
+  - Can users customize the fields in Argilla to fit their specific annotation needs?
+- source_sentence: "=== \"Single condition\"\n\n=== \"Multiple conditions\"\n\nFilter\
+    \ by status\n\nYou can filter records based on their status. The status can be\
+    \ pending, draft, submitted, or discarded.\n\n```python\nimport argilla_sdk as\
+    \ rg\n\nclient = rg.Argilla(api_url=\"\", api_key=\"\")\n\nworkspace = client.workspaces(\"\
+    my_workspace\")\n\ndataset = client.datasets(name=\"my_dataset\", workspace=workspace)\n\
+    \nstatus_filter = rg.Query(\n    filter = rg.Filter((\"status\", \"==\", \"submitted\"\
+    ))\n)"
+  sentences:
+  - The submitted application was rejected due to incomplete documentation.
+  - How can I apply filters to records by their status in Argilla?
+  - Can Argilla's IntegerMetadataProperty support a range of integer values as metadata?
+- source_sentence: 'description: In this section, we will provide a step-by-step guide
+    to show how to filter and query a dataset.
+
+
+    Query, filter, and export records
+
+
+    This guide provides an overview of how to query and filter a dataset in Argilla
+    and export records.'
+  sentences:
+  - The new restaurant in town offers a unique filter coffee that is a must-try for
+    coffee enthusiasts.
+  - Is it possible to design a user role with tailored access permissions within Argilla?
+  - Can Argilla be employed to search and filter datasets based on particular requirements
+    or keywords?
+- source_sentence: 'hide: footer
+
+
+    Fields
+
+
+    Fields in Argilla are define the content of a record that will be reviewed by
+    a user.'
+  sentences:
+  - Is it possible for annotators to tailor Argilla's fields to their unique annotation
+    requirements?
+  - The tourists tried to hide their footprints in the sand as they walked along the
+    deserted beach.
+  - Can this partnership with Prolific provide researchers with a broader range of
+    annotators to draw from, enhancing the quality of their studies?
+- source_sentence: 'hide: footer
+
+
+    rg.Argilla
+
+
+    To interact with the Argilla server from python you can use the Argilla class.
+    The Argilla client is used to create, get, update, and delete all Argilla resources,
+    such as workspaces, users, datasets, and records.
+
+
+    Usage Examples
+
+
+    Connecting to an Argilla server
+
+
+    To connect to an Argilla server, instantiate the Argilla class and pass the api_url
+    of the server and the api_key to authenticate.
+
+
+    ```python
+
+    import argilla_sdk as rg'
+  sentences:
+  - Can the Argilla class be employed to streamline dataset administration tasks in
+    my Argilla server setup?
+  - Is it possible to create new data entries in my dataset via Argilla's annotation
+    tools?
+  - The Argilla flowers were blooming beautifully in the garden.
+pipeline_tag: sentence-similarity
+model-index:
+- name: BGE base ArgillaSDK Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.1326530612244898
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2857142857142857
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3877551020408163
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5204081632653061
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.1326530612244898
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.09523809523809525
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.07755102040816327
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05204081632653061
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1326530612244898
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2857142857142857
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3877551020408163
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5204081632653061
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.3086125494748455
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.24321752510528016
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.26038538311827203
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.10204081632653061
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2755102040816326
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3877551020408163
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5102040816326531
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.10204081632653061
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.09183673469387756
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.07755102040816327
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05102040816326531
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.10204081632653061
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2755102040816326
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3877551020408163
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5102040816326531
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.29420081448590024
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.22640913508260446
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.24259809105769914
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.12244897959183673
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2755102040816326
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3877551020408163
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.12244897959183673
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.09183673469387753
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.07755102040816327
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.049999999999999996
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.12244897959183673
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2755102040816326
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3877551020408163
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2931450934182018
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.2290937803692905
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.24454883014070852
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.09183673469387756
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.25510204081632654
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3163265306122449
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.46938775510204084
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.09183673469387756
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.08503401360544219
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06326530612244897
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.046938775510204075
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.09183673469387756
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.25510204081632654
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3163265306122449
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.46938775510204084
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2629197762336244
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1992265954000647
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2164845577697655
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.08163265306122448
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.25510204081632654
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3163265306122449
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.47959183673469385
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.08163265306122448
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.08503401360544219
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06326530612244897
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04795918367346938
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.08163265306122448
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.25510204081632654
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3163265306122449
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.47959183673469385
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2610977190273289
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.19399497894395853
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.20591442395637935
+      name: Cosine Map@100
+---
+
+# BGE base ArgillaSDK Matryoshka
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): 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("plaguss/bge-base-argilla-sdk-matryoshka")
+# Run inference
+sentences = [
+    'hide: footer\n\nrg.Argilla\n\nTo interact with the Argilla server from python you can use the Argilla class. The Argilla client is used to create, get, update, and delete all Argilla resources, such as workspaces, users, datasets, and records.\n\nUsage Examples\n\nConnecting to an Argilla server\n\nTo connect to an Argilla server, instantiate the Argilla class and pass the api_url of the server and the api_key to authenticate.\n\n```python\nimport argilla_sdk as rg',
+    'Can the Argilla class be employed to streamline dataset administration tasks in my Argilla server setup?',
+    'The Argilla flowers were blooming beautifully in the garden.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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
+
+#### Information Retrieval
+* Dataset: `dim_768`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1327     |
+| cosine_accuracy@3   | 0.2857     |
+| cosine_accuracy@5   | 0.3878     |
+| cosine_accuracy@10  | 0.5204     |
+| cosine_precision@1  | 0.1327     |
+| cosine_precision@3  | 0.0952     |
+| cosine_precision@5  | 0.0776     |
+| cosine_precision@10 | 0.052      |
+| cosine_recall@1     | 0.1327     |
+| cosine_recall@3     | 0.2857     |
+| cosine_recall@5     | 0.3878     |
+| cosine_recall@10    | 0.5204     |
+| cosine_ndcg@10      | 0.3086     |
+| cosine_mrr@10       | 0.2432     |
+| **cosine_map@100**  | **0.2604** |
+
+#### Information Retrieval
+* Dataset: `dim_512`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.102      |
+| cosine_accuracy@3   | 0.2755     |
+| cosine_accuracy@5   | 0.3878     |
+| cosine_accuracy@10  | 0.5102     |
+| cosine_precision@1  | 0.102      |
+| cosine_precision@3  | 0.0918     |
+| cosine_precision@5  | 0.0776     |
+| cosine_precision@10 | 0.051      |
+| cosine_recall@1     | 0.102      |
+| cosine_recall@3     | 0.2755     |
+| cosine_recall@5     | 0.3878     |
+| cosine_recall@10    | 0.5102     |
+| cosine_ndcg@10      | 0.2942     |
+| cosine_mrr@10       | 0.2264     |
+| **cosine_map@100**  | **0.2426** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1224     |
+| cosine_accuracy@3   | 0.2755     |
+| cosine_accuracy@5   | 0.3878     |
+| cosine_accuracy@10  | 0.5        |
+| cosine_precision@1  | 0.1224     |
+| cosine_precision@3  | 0.0918     |
+| cosine_precision@5  | 0.0776     |
+| cosine_precision@10 | 0.05       |
+| cosine_recall@1     | 0.1224     |
+| cosine_recall@3     | 0.2755     |
+| cosine_recall@5     | 0.3878     |
+| cosine_recall@10    | 0.5        |
+| cosine_ndcg@10      | 0.2931     |
+| cosine_mrr@10       | 0.2291     |
+| **cosine_map@100**  | **0.2445** |
+
+#### Information Retrieval
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0918     |
+| cosine_accuracy@3   | 0.2551     |
+| cosine_accuracy@5   | 0.3163     |
+| cosine_accuracy@10  | 0.4694     |
+| cosine_precision@1  | 0.0918     |
+| cosine_precision@3  | 0.085      |
+| cosine_precision@5  | 0.0633     |
+| cosine_precision@10 | 0.0469     |
+| cosine_recall@1     | 0.0918     |
+| cosine_recall@3     | 0.2551     |
+| cosine_recall@5     | 0.3163     |
+| cosine_recall@10    | 0.4694     |
+| cosine_ndcg@10      | 0.2629     |
+| cosine_mrr@10       | 0.1992     |
+| **cosine_map@100**  | **0.2165** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0816     |
+| cosine_accuracy@3   | 0.2551     |
+| cosine_accuracy@5   | 0.3163     |
+| cosine_accuracy@10  | 0.4796     |
+| cosine_precision@1  | 0.0816     |
+| cosine_precision@3  | 0.085      |
+| cosine_precision@5  | 0.0633     |
+| cosine_precision@10 | 0.048      |
+| cosine_recall@1     | 0.0816     |
+| cosine_recall@3     | 0.2551     |
+| cosine_recall@5     | 0.3163     |
+| cosine_recall@10    | 0.4796     |
+| cosine_ndcg@10      | 0.2611     |
+| cosine_mrr@10       | 0.194      |
+| **cosine_map@100**  | **0.2059** |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+
+* Size: 882 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 90.85 tokens</li><li>max: 198 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 25.44 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 22.33 tokens</li><li>max: 61 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | positive                                                                                                                                                                | negative                                                                                                                                                                                                      |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>``<br>!!! note "Update the metadata"<br>    ThemetadataofRecordobject is a python dictionary. So to update the metadata of a record, you can iterate over the records and update the metadata by key or usingmetadata.update`. After that, you should update the records in the dataset.</code>                                                                                                                                                                                                       | <code>Can I use Argilla to annotate the metadata of Record objects and update them in the dataset?</code>                                                               | <code>The beautiful scenery of the Argilla valley in Italy is perfect for a relaxing summer vacation.</code>                                                                                                  |
+  | <code>git checkout [branch-name]<br>git rebase [default-branch]<br>```<br><br>If everything is right, we need to commit and push the changes to your fork. For that, run the following commands:<br><br>```sh<br><br>Add the changes to the staging area<br><br>git add filename<br><br>Commit the changes by writing a proper message<br><br>git commit -m "commit-message"<br><br>Push the changes to your fork</code>                                                                                    | <code>Can I commit Argilla's annotation changes and push them to a forked project repository after rebasing from the default branch?</code>                             | <code>The beautiful beach in Argilla, Spain, is a popular spot for surfers to catch a wave and enjoy the sunny weather.</code>                                                                                |
+  | <code>Accessing Record Attributes<br><br>The Record object has suggestions, responses, metadata, and vectors attributes that can be accessed directly whilst iterating over records in a dataset.<br><br>python<br>for record in dataset.records(<br>    with_suggestions=True,<br>    with_responses=True,<br>    with_metadata=True,<br>    with_vectors=True<br>    ):<br>    print(record.suggestions)<br>    print(record.responses)<br>    print(record.metadata)<br>    print(record.vectors)</code> | <code>Is it possible to retrieve the suggestions, responses, metadata, and vectors of a Record object at the same time when iterating over a dataset in Argilla?</code> | <code>The new hiking trail offered breathtaking suggestions for scenic views, responses to environmental concerns, and metadata about the surrounding ecosystem, but it lacked vectors for navigation.</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "TripletLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_eval_batch_size`: 4
+- `gradient_accumulation_steps`: 4
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 4
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 4
+- `eval_accumulation_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`: cosine
+- `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`: False
+- `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`: True
+- `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}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch     | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
+|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.1802    | 5      | 21.701        | -                      | -                      | -                      | -                     | -                      |
+| 0.3604    | 10     | 21.7449       | -                      | -                      | -                      | -                     | -                      |
+| 0.5405    | 15     | 21.7453       | -                      | -                      | -                      | -                     | -                      |
+| 0.7207    | 20     | 21.7168       | -                      | -                      | -                      | -                     | -                      |
+| 0.9009    | 25     | 21.6945       | -                      | -                      | -                      | -                     | -                      |
+| **0.973** | **27** | **-**         | **0.2165**             | **0.2445**             | **0.2426**             | **0.2059**            | **0.2604**             |
+| 1.0811    | 30     | 21.7248       | -                      | -                      | -                      | -                     | -                      |
+| 1.2613    | 35     | 21.7322       | -                      | -                      | -                      | -                     | -                      |
+| 1.4414    | 40     | 21.7367       | -                      | -                      | -                      | -                     | -                      |
+| 1.6216    | 45     | 21.6821       | -                      | -                      | -                      | -                     | -                      |
+| 1.8018    | 50     | 21.8392       | -                      | -                      | -                      | -                     | -                      |
+| 1.9820    | 55     | 21.6441       | 0.2165                 | 0.2445                 | 0.2426                 | 0.2059                | 0.2604                 |
+| 2.1622    | 60     | 21.8154       | -                      | -                      | -                      | -                     | -                      |
+| 2.3423    | 65     | 21.7098       | -                      | -                      | -                      | -                     | -                      |
+| 2.5225    | 70     | 21.6447       | -                      | -                      | -                      | -                     | -                      |
+| 2.7027    | 75     | 21.6033       | -                      | -                      | -                      | -                     | -                      |
+| 2.8829    | 80     | 21.8271       | -                      | -                      | -                      | -                     | -                      |
+| 2.9189    | 81     | -             | 0.2165                 | 0.2445                 | 0.2426                 | 0.2059                | 0.2604                 |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.8
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2
+- Accelerate: 0.31.0
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning}, 
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:0d4db737f56aaea90796b5a8d219de0eee958295a575c611f6b417ad340151da
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}