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

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+---
+base_model: BAAI/bge-base-en-v1.5
+datasets: []
+language: []
+library_name: sentence-transformers
+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
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6300
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: As of January 31, 2023, the weighted average remaining lease term
+    for operating leases was 7 years and for finance leases was 3 years.
+  sentences:
+  - What was the Company's net deferred tax assets as of December 30, 2023, and December
+    31, 2022?
+  - What were the weighted average remaining lease terms for operating and finance
+    leases as of January 31, 2023?
+  - How much did the net investment income change from 2021 to 2023?
+- source_sentence: The 4.500% notes due in August 2034 have an interest rate of 4.55%.
+  sentences:
+  - What types of insurance coverage does the company provide to its employees at
+    no premium cost, as part of their general employee benefits package?
+  - What is the interest rate for the 4.500% notes due in August 2034?
+  - How much did the company's revenues decrease in 2023 compared to 2022?
+- source_sentence: In 2023, other income (expense), net included $376 million of interest
+    income, partially offset by $167 million of net unrealized losses on equity investments.
+    Other income (expense), net in 2022 included $657 million of net unrealized losses
+    on equity investments, partially offset by $106 million of interest income.
+  sentences:
+  - What contributed to the net other income (expense) in 2023?
+  - What types of products does the Canada operation offer?
+  - What was the net change in cash and cash equivalents in 2022?
+- source_sentence: We believe the claims in these cases are without merit and are
+    vigorously defending these lawsuits.
+  sentences:
+  - Where in the Annual Report can one find a description of certain legal matters
+    and their impact on the company?
+  - What is the goal of the company regarding its global corporate operations by 2030?
+  - What is the stance of the defending airlines on the claims made against them in
+    the capacity antitrust litigation?
+- source_sentence: North America's total net revenues for the fiscal year ended October
+    1, 2023, were $26,569.6 million.
+  sentences:
+  - What was the total net revenue for North America in fiscal 2023?
+  - What are the consequences of impermissible use or disclosure of PHI according
+    to the HITECH Act?
+  - What does the index in a financial report indicate?
+model-index:
+- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6171428571428571
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7457142857142857
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8114285714285714
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8585714285714285
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6171428571428571
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24857142857142858
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16228571428571428
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08585714285714285
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6171428571428571
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7457142857142857
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8114285714285714
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8585714285714285
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7357204832416036
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6965260770975052
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7015509951793545
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6214285714285714
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.74
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8642857142857143
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6214285714285714
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24666666666666665
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15999999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08642857142857142
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6214285714285714
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.74
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8642857142857143
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.738181682287809
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6983236961451246
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7027820040111107
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7271428571428571
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7928571428571428
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8442857142857143
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24238095238095236
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.15857142857142856
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08442857142857142
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7271428571428571
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7928571428571428
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8442857142857143
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7182448637999702
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6782879818594099
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.683606591058064
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5728571428571428
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7014285714285714
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7557142857142857
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8157142857142857
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5728571428571428
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2338095238095238
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1511428571428571
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08157142857142856
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5728571428571428
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7014285714285714
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7557142857142857
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8157142857142857
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6915163160852085
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6521536281179136
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6580414471513885
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5142857142857142
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.6371428571428571
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6728571428571428
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7357142857142858
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5142857142857142
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.21238095238095234
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.13457142857142856
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07357142857142857
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5142857142857142
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.6371428571428571
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6728571428571428
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7357142857142858
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6197107516374883
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5832369614512468
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5907376271746598
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-base-en-v1.5
+
+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:** 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': 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("ethan-ky/bge-base-financial-matryoshka")
+# Run inference
+sentences = [
+    "North America's total net revenues for the fiscal year ended October 1, 2023, were $26,569.6 million.",
+    'What was the total net revenue for North America in fiscal 2023?',
+    'What are the consequences of impermissible use or disclosure of PHI according to the HITECH Act?',
+]
+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   | 0.6171     |
+| cosine_accuracy@3   | 0.7457     |
+| cosine_accuracy@5   | 0.8114     |
+| cosine_accuracy@10  | 0.8586     |
+| cosine_precision@1  | 0.6171     |
+| cosine_precision@3  | 0.2486     |
+| cosine_precision@5  | 0.1623     |
+| cosine_precision@10 | 0.0859     |
+| cosine_recall@1     | 0.6171     |
+| cosine_recall@3     | 0.7457     |
+| cosine_recall@5     | 0.8114     |
+| cosine_recall@10    | 0.8586     |
+| cosine_ndcg@10      | 0.7357     |
+| cosine_mrr@10       | 0.6965     |
+| **cosine_map@100**  | **0.7016** |
+
+#### 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   | 0.6214     |
+| cosine_accuracy@3   | 0.74       |
+| cosine_accuracy@5   | 0.8        |
+| cosine_accuracy@10  | 0.8643     |
+| cosine_precision@1  | 0.6214     |
+| cosine_precision@3  | 0.2467     |
+| cosine_precision@5  | 0.16       |
+| cosine_precision@10 | 0.0864     |
+| cosine_recall@1     | 0.6214     |
+| cosine_recall@3     | 0.74       |
+| cosine_recall@5     | 0.8        |
+| cosine_recall@10    | 0.8643     |
+| cosine_ndcg@10      | 0.7382     |
+| cosine_mrr@10       | 0.6983     |
+| **cosine_map@100**  | **0.7028** |
+
+#### 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   | 0.6        |
+| cosine_accuracy@3   | 0.7271     |
+| cosine_accuracy@5   | 0.7929     |
+| cosine_accuracy@10  | 0.8443     |
+| cosine_precision@1  | 0.6        |
+| cosine_precision@3  | 0.2424     |
+| cosine_precision@5  | 0.1586     |
+| cosine_precision@10 | 0.0844     |
+| cosine_recall@1     | 0.6        |
+| cosine_recall@3     | 0.7271     |
+| cosine_recall@5     | 0.7929     |
+| cosine_recall@10    | 0.8443     |
+| cosine_ndcg@10      | 0.7182     |
+| cosine_mrr@10       | 0.6783     |
+| **cosine_map@100**  | **0.6836** |
+
+#### 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   | 0.5729    |
+| cosine_accuracy@3   | 0.7014    |
+| cosine_accuracy@5   | 0.7557    |
+| cosine_accuracy@10  | 0.8157    |
+| cosine_precision@1  | 0.5729    |
+| cosine_precision@3  | 0.2338    |
+| cosine_precision@5  | 0.1511    |
+| cosine_precision@10 | 0.0816    |
+| cosine_recall@1     | 0.5729    |
+| cosine_recall@3     | 0.7014    |
+| cosine_recall@5     | 0.7557    |
+| cosine_recall@10    | 0.8157    |
+| cosine_ndcg@10      | 0.6915    |
+| cosine_mrr@10       | 0.6522    |
+| **cosine_map@100**  | **0.658** |
+
+#### 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   | 0.5143     |
+| cosine_accuracy@3   | 0.6371     |
+| cosine_accuracy@5   | 0.6729     |
+| cosine_accuracy@10  | 0.7357     |
+| cosine_precision@1  | 0.5143     |
+| cosine_precision@3  | 0.2124     |
+| cosine_precision@5  | 0.1346     |
+| cosine_precision@10 | 0.0736     |
+| cosine_recall@1     | 0.5143     |
+| cosine_recall@3     | 0.6371     |
+| cosine_recall@5     | 0.6729     |
+| cosine_recall@10    | 0.7357     |
+| cosine_ndcg@10      | 0.6197     |
+| cosine_mrr@10       | 0.5832     |
+| **cosine_map@100**  | **0.5907** |
+
+<!--
+## 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: 6,300 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                           | anchor                                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 45.35 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.67 tokens</li><li>max: 46 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | anchor                                                                                                          |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------|
+  | <code>Our ability to develop and operate units at the right locations and to deliver a customer-centric omni-channel experience largely determines our competitive position within the retail industry. We believe price leadership is a critical part of our business model and we continue to focus on moving our markets towards an EDLP approach. Additionally, our ability to operate food departments effectively has a significant impact on our competitive position in the markets where we operate.</code> | <code>What factors contribute to Walmart International's competitive position?</code>                           |
+  | <code>tax annual aggregate losses incurred in any year from U.S. hurricane events could be in excess of $3,827 million (or 6.4 percent of total Chubb shareholders’ equity at December 31, 2023).</code>                                                                                                                                                                                                                                                                                                             | <code>What is the expected maximum potential loss from hurricane events for Chubb as of the end of 2023?</code> |
+  | <code>The 'Glossary of Terms and Acronyms’ is included on pages 315-321.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>What is included on pages 315 to 321 of the document?</code>                                              |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "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_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### 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`: 32
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `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`: 4
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `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_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`: 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`: no_duplicates
+- `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 |
+|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.8122     | 10     | 1.3939        | -                      | -                      | -                      | -                     | -                      |
+| **0.9746** | **12** | **-**         | **0.658**              | **0.6836**             | **0.7028**             | **0.5907**            | **0.7016**             |
+| 1.6244     | 20     | 1.3574        | -                      | -                      | -                      | -                     | -                      |
+| 1.9492     | 24     | -             | 0.6580                 | 0.6836                 | 0.7028                 | 0.5907                | 0.7016                 |
+| 2.4365     | 30     | 1.3485        | -                      | -                      | -                      | -                     | -                      |
+| 2.9239     | 36     | -             | 0.6580                 | 0.6836                 | 0.7028                 | 0.5907                | 0.7016                 |
+| 3.2487     | 40     | 1.3606        | -                      | -                      | -                      | -                     | -                      |
+| 3.8985     | 48     | -             | 0.6580                 | 0.6836                 | 0.7028                 | 0.5907                | 0.7016                 |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.9.19
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.19.1
+- 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}
+}
+```
+
+#### 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.*
+-->

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+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+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": {
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+      "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]"
+}