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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+---
+language:
+- en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:1K<n<10K
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-base-en-v1.5
+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: What begins on page 105 of this report?
+  sentences:
+  - What sections are included alongside the Financial Statements in this report?
+  - How did net revenues change from 2021 to 2022 on a FX-Neutral basis?
+  - How much did MedTech's sales increase in 2023 compared to 2022?
+- source_sentence: When does the Company's fiscal year end?
+  sentences:
+  - What was the total store count for the company at the end of fiscal 2022?
+  - What was the total revenue for all UnitedHealthcare services in 2023?
+  - What were the main factors contributing to the increase in net income in 2023?
+- source_sentence: AutoZone, Inc. began operations in 1979.
+  sentences:
+  - When did AutoZone, Inc. begin its operations?
+  - Mr. Pleas was named Senior Vice President and Controller during 2007.
+  - Which item discusses Financial Statements and Supplementary Data?
+- source_sentence: Are the ESG goals guaranteed to be met?
+  sentences:
+  - What measures is the company implementing to support climate goals?
+  - What types of diseases does Gilead Sciences, Inc. focus on treating?
+  - Changes in foreign exchange rates reduced cost of sales by $254 million in 2023.
+- source_sentence: What was Gilead's total revenue in 2023?
+  sentences:
+  - What was the total revenue for the year ended December 31, 2023?
+  - How much was the impairment related to the CAT loan receivable in 2023?
+  - What are some of the critical accounting policies that affect financial statements?
+pipeline_tag: sentence-similarity
+model-index:
+- name: BGE base Financial Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: basline 768
+      type: basline_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7085714285714285
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8514285714285714
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8842857142857142
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9271428571428572
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7085714285714285
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2838095238095238
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17685714285714282
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09271428571428571
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7085714285714285
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8514285714285714
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8842857142857142
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9271428571428572
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8214972164555796
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7873509070294781
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.790665594958196
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: basline 512
+      type: basline_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7114285714285714
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.85
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8828571428571429
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9228571428571428
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7114285714285714
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2833333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17657142857142855
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09228571428571428
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7114285714285714
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.85
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8828571428571429
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9228571428571428
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.820942296767774
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7878956916099771
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7915593121031292
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: basline 256
+      type: basline_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7057142857142857
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8414285714285714
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.88
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9228571428571428
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7057142857142857
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.28047619047619043
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.176
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09228571428571428
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7057142857142857
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8414285714285714
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.88
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9228571428571428
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8161680075424235
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7817953514739227
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.785367816349997
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: basline 128
+      type: basline_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7028571428571428
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8342857142857143
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8742857142857143
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9171428571428571
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7028571428571428
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27809523809523806
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17485714285714282
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09171428571428569
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7028571428571428
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8342857142857143
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8742857142857143
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9171428571428571
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8109319521599055
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7768752834467119
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7802736634060462
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: basline 64
+      type: basline_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6728571428571428
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8171428571428572
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8614285714285714
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9014285714285715
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6728571428571428
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2723809523809524
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17228571428571426
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09014285714285714
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6728571428571428
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8171428571428572
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8614285714285714
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9014285714285715
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7900026049536226
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7539795918367346
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7582240178397145
+      name: Cosine Map@100
+---
+
+# BGE base Financial 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("philschmid/bge-base-financial-matryoshka")
+# Run inference
+sentences = [
+    "What was Gilead's total revenue in 2023?",
+    'What was the total revenue for the year ended December 31, 2023?',
+    'How much was the impairment related to the CAT loan receivable in 2023?',
+]
+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: `basline_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   | 0.7086     |
+| cosine_accuracy@3   | 0.8514     |
+| cosine_accuracy@5   | 0.8843     |
+| cosine_accuracy@10  | 0.9271     |
+| cosine_precision@1  | 0.7086     |
+| cosine_precision@3  | 0.2838     |
+| cosine_precision@5  | 0.1769     |
+| cosine_precision@10 | 0.0927     |
+| cosine_recall@1     | 0.7086     |
+| cosine_recall@3     | 0.8514     |
+| cosine_recall@5     | 0.8843     |
+| cosine_recall@10    | 0.9271     |
+| cosine_ndcg@10      | 0.8215     |
+| cosine_mrr@10       | 0.7874     |
+| **cosine_map@100**  | **0.7907** |
+
+#### Information Retrieval
+* Dataset: `basline_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   | 0.7114     |
+| cosine_accuracy@3   | 0.85       |
+| cosine_accuracy@5   | 0.8829     |
+| cosine_accuracy@10  | 0.9229     |
+| cosine_precision@1  | 0.7114     |
+| cosine_precision@3  | 0.2833     |
+| cosine_precision@5  | 0.1766     |
+| cosine_precision@10 | 0.0923     |
+| cosine_recall@1     | 0.7114     |
+| cosine_recall@3     | 0.85       |
+| cosine_recall@5     | 0.8829     |
+| cosine_recall@10    | 0.9229     |
+| cosine_ndcg@10      | 0.8209     |
+| cosine_mrr@10       | 0.7879     |
+| **cosine_map@100**  | **0.7916** |
+
+#### Information Retrieval
+* Dataset: `basline_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   | 0.7057     |
+| cosine_accuracy@3   | 0.8414     |
+| cosine_accuracy@5   | 0.88       |
+| cosine_accuracy@10  | 0.9229     |
+| cosine_precision@1  | 0.7057     |
+| cosine_precision@3  | 0.2805     |
+| cosine_precision@5  | 0.176      |
+| cosine_precision@10 | 0.0923     |
+| cosine_recall@1     | 0.7057     |
+| cosine_recall@3     | 0.8414     |
+| cosine_recall@5     | 0.88       |
+| cosine_recall@10    | 0.9229     |
+| cosine_ndcg@10      | 0.8162     |
+| cosine_mrr@10       | 0.7818     |
+| **cosine_map@100**  | **0.7854** |
+
+#### Information Retrieval
+* Dataset: `basline_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   | 0.7029     |
+| cosine_accuracy@3   | 0.8343     |
+| cosine_accuracy@5   | 0.8743     |
+| cosine_accuracy@10  | 0.9171     |
+| cosine_precision@1  | 0.7029     |
+| cosine_precision@3  | 0.2781     |
+| cosine_precision@5  | 0.1749     |
+| cosine_precision@10 | 0.0917     |
+| cosine_recall@1     | 0.7029     |
+| cosine_recall@3     | 0.8343     |
+| cosine_recall@5     | 0.8743     |
+| cosine_recall@10    | 0.9171     |
+| cosine_ndcg@10      | 0.8109     |
+| cosine_mrr@10       | 0.7769     |
+| **cosine_map@100**  | **0.7803** |
+
+#### Information Retrieval
+* Dataset: `basline_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   | 0.6729     |
+| cosine_accuracy@3   | 0.8171     |
+| cosine_accuracy@5   | 0.8614     |
+| cosine_accuracy@10  | 0.9014     |
+| cosine_precision@1  | 0.6729     |
+| cosine_precision@3  | 0.2724     |
+| cosine_precision@5  | 0.1723     |
+| cosine_precision@10 | 0.0901     |
+| cosine_recall@1     | 0.6729     |
+| cosine_recall@3     | 0.8171     |
+| cosine_recall@5     | 0.8614     |
+| cosine_recall@10    | 0.9014     |
+| cosine_ndcg@10      | 0.79       |
+| cosine_mrr@10       | 0.754      |
+| **cosine_map@100**  | **0.7582** |
+
+<!--
+## 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: 10 tokens</li><li>mean: 46.11 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.26 tokens</li><li>max: 43 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                          | anchor                                                                                             |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
+  | <code>Fiscal 2023 total gross profit margin of 35.1% represents an increase of 1.7 percentage points as compared to the respective prior year period.</code>                                                                                                                                      | <code>What was the total gross profit margin for Hewlett Packard Enterprise in fiscal 2023?</code> |
+  | <code>Noninterest expense increased to $65.8 billion in 2023, primarily due to higher investments in people and technology and higher FDIC expense, including $2.1 billion for the estimated special assessment amount arising from the closure of Silicon Valley Bank and Signature Bank.</code> | <code>What was the total noninterest expense for the company in 2023?</code>                       |
+  | <code>As of May 31, 2022, FedEx Office had approximately 12,000 employees.</code>                                                                                                                                                                                                                 | <code>How many employees did FedEx Office have as of May 31, 2023?</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
+- `sanity_evaluation`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step   | Training Loss | basline_128_cosine_map@100 | basline_256_cosine_map@100 | basline_512_cosine_map@100 | basline_64_cosine_map@100 | basline_768_cosine_map@100 |
+|:----------:|:------:|:-------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-------------------------:|:--------------------------:|
+| 0.8122     | 10     | 1.5259        | -                          | -                          | -                          | -                         | -                          |
+| 0.9746     | 12     | -             | 0.7502                     | 0.7737                     | 0.7827                     | 0.7185                    | 0.7806                     |
+| 1.6244     | 20     | 0.6545        | -                          | -                          | -                          | -                         | -                          |
+| **1.9492** | **24** | **-**         | **0.7689**                 | **0.7844**                 | **0.7869**                 | **0.7447**                | **0.7909**                 |
+| 2.4365     | 30     | 0.4784        | -                          | -                          | -                          | -                         | -                          |
+| 2.9239     | 36     | -             | 0.7733                     | 0.7916                     | 0.7904                     | 0.7491                    | 0.7930                     |
+| 3.2487     | 40     | 0.3827        | -                          | -                          | -                          | -                         | -                          |
+| 3.8985     | 48     | -             | 0.7739                     | 0.7907                     | 0.7900                     | 0.7479                    | 0.7948                     |
+| 0.8122     | 10     | 0.2685        | -                          | -                          | -                          | -                         | -                          |
+| 0.9746     | 12     | -             | 0.7779                     | 0.7932                     | 0.7948                     | 0.7517                    | 0.7943                     |
+| 1.6244     | 20     | 0.183         | -                          | -                          | -                          | -                         | -                          |
+| **1.9492** | **24** | **-**         | **0.7784**                 | **0.7929**                 | **0.7963**                 | **0.7575**                | **0.7957**                 |
+| 2.4365     | 30     | 0.1877        | -                          | -                          | -                          | -                         | -                          |
+| 2.9239     | 36     | -             | 0.7814                     | 0.7914                     | 0.7992                     | 0.7570                    | 0.7974                     |
+| 3.2487     | 40     | 0.1826        | -                          | -                          | -                          | -                         | -                          |
+| 3.8985     | 48     | -             | 0.7818                     | 0.7916                     | 0.7976                     | 0.7580                    | 0.7960                     |
+| 0.8122     | 10     | 0.071         | -                          | -                          | -                          | -                         | -                          |
+| 0.9746     | 12     | -             | 0.7810                     | 0.7935                     | 0.7954                     | 0.7550                    | 0.7949                     |
+| 1.6244     | 20     | 0.0629        | -                          | -                          | -                          | -                         | -                          |
+| **1.9492** | **24** | **-**         | **0.7855**                 | **0.7914**                 | **0.7989**                 | **0.7559**                | **0.7981**                 |
+| 2.4365     | 30     | 0.0827        | -                          | -                          | -                          | -                         | -                          |
+| 2.9239     | 36     | -             | 0.7893                     | 0.7927                     | 0.7987                     | 0.7539                    | 0.7961                     |
+| 3.2487     | 40     | 0.1003        | -                          | -                          | -                          | -                         | -                          |
+| 3.8985     | 48     | -             | 0.7903                     | 0.7915                     | 0.7980                     | 0.7530                    | 0.7951                     |
+| 0.8122     | 10     | 0.0213        | -                          | -                          | -                          | -                         | -                          |
+| 0.9746     | 12     | -             | 0.7786                     | 0.7869                     | 0.7885                     | 0.7566                    | 0.7908                     |
+| 1.6244     | 20     | 0.0234        | -                          | -                          | -                          | -                         | -                          |
+| **1.9492** | **24** | **-**         | **0.783**                  | **0.7882**                 | **0.793**                  | **0.7551**                | **0.7946**                 |
+| 2.4365     | 30     | 0.0357        | -                          | -                          | -                          | -                         | -                          |
+| 2.9239     | 36     | -             | 0.7838                     | 0.7892                     | 0.7922                     | 0.7579                    | 0.7907                     |
+| 3.2487     | 40     | 0.0563        | -                          | -                          | -                          | -                         | -                          |
+| 3.8985     | 48     | -             | 0.7846                     | 0.7887                     | 0.7912                     | 0.7582                    | 0.7901                     |
+| 0.8122     | 10     | 0.0075        | -                          | -                          | -                          | -                         | -                          |
+| 0.9746     | 12     | -             | 0.7730                     | 0.7816                     | 0.7818                     | 0.7550                    | 0.7868                     |
+| 1.6244     | 20     | 0.01          | -                          | -                          | -                          | -                         | -                          |
+| **1.9492** | **24** | **-**         | **0.7827**                 | **0.785**                  | **0.7896**                 | **0.7551**                | **0.7915**                 |
+| 2.4365     | 30     | 0.0154        | -                          | -                          | -                          | -                         | -                          |
+| 2.9239     | 36     | -             | 0.7808                     | 0.7838                     | 0.7921                     | 0.7584                    | 0.7916                     |
+| 3.2487     | 40     | 0.0312        | -                          | -                          | -                          | -                         | -                          |
+| 3.8985     | 48     | -             | 0.7803                     | 0.7854                     | 0.7916                     | 0.7582                    | 0.7907                     |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.0
+- Transformers: 4.42.0.dev0
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.29.2
+- 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": "4.42.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.28.1",
+    "pytorch": "1.13.0+cu117"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:056de75731b8c5f3c4540d8064159852263cee5e15b59170f3a223191e0f3b71
+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]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "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]"
+}