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 +1,789 @@
+---
+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 types of industries does TTI service?
+  sentences:
+  - What types of businesses does HPE serve?
+  - How much did the company's revenues decrease in 2023 compared to 2022?
+  - By what percentage did the quarterly cash dividend increase on January 26, 2023?
+- source_sentence: What does ITEM 8 in Form 10-K refer to?
+  sentences:
+  - ITEM 8 in Form 10-K refers to the Financial Statements and Supplementary Data.
+  - UnitedHealth Group reported net earnings of $23,144 million in 2023.
+  - What factors contributed to the decrease in automotive leasing revenue in 2023?
+- source_sentence: What are consolidated financial statements?
+  sentences:
+  - The report on the Consolidated Financial Statements is dated February 16, 2024.
+  - How much did the foreclosed properties decrease in value during 2023?
+  - What was Chipotle Mexican Grill's net income in 2023?
+- source_sentence: What were the total product sales in 2023?
+  sentences:
+  - Total product sales in 2023 amounted to $27,305 million.
+  - How does AutoZone manage its foreign operations in terms of currency?
+  - What restrictions does the Bank Holding Company Act impose on JPMorgan Chase?
+- source_sentence: What is the global presence of Lubrizol?
+  sentences:
+  - How does The Coca-Cola Company distribute its beverage products globally?
+  - What are the two operating segments of NVIDIA as mentioned in the text?
+  - How much did Delta Air Lines spend on debt and finance lease obligations in 2023?
+pipeline_tag: sentence-similarity
+model-index:
+- name: BGE base Financial Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6957142857142857
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8342857142857143
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8628571428571429
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9085714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6957142857142857
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2780952380952381
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17257142857142854
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09085714285714284
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6957142857142857
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8342857142857143
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8628571428571429
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9085714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8045138729797765
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7709591836734694
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7746687336147619
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8271428571428572
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8642857142857143
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9157142857142857
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2757142857142857
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17285714285714285
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09157142857142857
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8271428571428572
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8642857142857143
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9157142857142857
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.807258910509631
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7726218820861678
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7757170101327764
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6928571428571428
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.82
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8585714285714285
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9028571428571428
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6928571428571428
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2733333333333334
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1717142857142857
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09028571428571427
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6928571428571428
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.82
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8585714285714285
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9028571428571428
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7979490809476271
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7643027210884353
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7684617620062486
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6857142857142857
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.81
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8542857142857143
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.89
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6857142857142857
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17085714285714282
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.089
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6857142857142857
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.81
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8542857142857143
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.89
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7877753635329912
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7549472789115641
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7596045003108374
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6528571428571428
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7571428571428571
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8185714285714286
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8685714285714285
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6528571428571428
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2523809523809524
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1637142857142857
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08685714285714284
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6528571428571428
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7571428571428571
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8185714285714286
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8685714285714285
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7557078446701566
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7201400226757368
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7249497855774768
+      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("Andresckamilo/bge-base-financial-matryoshka")
+# Run inference
+sentences = [
+    'What is the global presence of Lubrizol?',
+    'How does The Coca-Cola Company distribute its beverage products globally?',
+    'What are the two operating segments of NVIDIA as mentioned in the text?',
+]
+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   | 0.6957     |
+| cosine_accuracy@3   | 0.8343     |
+| cosine_accuracy@5   | 0.8629     |
+| cosine_accuracy@10  | 0.9086     |
+| cosine_precision@1  | 0.6957     |
+| cosine_precision@3  | 0.2781     |
+| cosine_precision@5  | 0.1726     |
+| cosine_precision@10 | 0.0909     |
+| cosine_recall@1     | 0.6957     |
+| cosine_recall@3     | 0.8343     |
+| cosine_recall@5     | 0.8629     |
+| cosine_recall@10    | 0.9086     |
+| cosine_ndcg@10      | 0.8045     |
+| cosine_mrr@10       | 0.771      |
+| **cosine_map@100**  | **0.7747** |
+
+#### 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   | 0.7        |
+| cosine_accuracy@3   | 0.8271     |
+| cosine_accuracy@5   | 0.8643     |
+| cosine_accuracy@10  | 0.9157     |
+| cosine_precision@1  | 0.7        |
+| cosine_precision@3  | 0.2757     |
+| cosine_precision@5  | 0.1729     |
+| cosine_precision@10 | 0.0916     |
+| cosine_recall@1     | 0.7        |
+| cosine_recall@3     | 0.8271     |
+| cosine_recall@5     | 0.8643     |
+| cosine_recall@10    | 0.9157     |
+| cosine_ndcg@10      | 0.8073     |
+| cosine_mrr@10       | 0.7726     |
+| **cosine_map@100**  | **0.7757** |
+
+#### 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   | 0.6929     |
+| cosine_accuracy@3   | 0.82       |
+| cosine_accuracy@5   | 0.8586     |
+| cosine_accuracy@10  | 0.9029     |
+| cosine_precision@1  | 0.6929     |
+| cosine_precision@3  | 0.2733     |
+| cosine_precision@5  | 0.1717     |
+| cosine_precision@10 | 0.0903     |
+| cosine_recall@1     | 0.6929     |
+| cosine_recall@3     | 0.82       |
+| cosine_recall@5     | 0.8586     |
+| cosine_recall@10    | 0.9029     |
+| cosine_ndcg@10      | 0.7979     |
+| cosine_mrr@10       | 0.7643     |
+| **cosine_map@100**  | **0.7685** |
+
+#### 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   | 0.6857     |
+| cosine_accuracy@3   | 0.81       |
+| cosine_accuracy@5   | 0.8543     |
+| cosine_accuracy@10  | 0.89       |
+| cosine_precision@1  | 0.6857     |
+| cosine_precision@3  | 0.27       |
+| cosine_precision@5  | 0.1709     |
+| cosine_precision@10 | 0.089      |
+| cosine_recall@1     | 0.6857     |
+| cosine_recall@3     | 0.81       |
+| cosine_recall@5     | 0.8543     |
+| cosine_recall@10    | 0.89       |
+| cosine_ndcg@10      | 0.7878     |
+| cosine_mrr@10       | 0.7549     |
+| **cosine_map@100**  | **0.7596** |
+
+#### 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   | 0.6529     |
+| cosine_accuracy@3   | 0.7571     |
+| cosine_accuracy@5   | 0.8186     |
+| cosine_accuracy@10  | 0.8686     |
+| cosine_precision@1  | 0.6529     |
+| cosine_precision@3  | 0.2524     |
+| cosine_precision@5  | 0.1637     |
+| cosine_precision@10 | 0.0869     |
+| cosine_recall@1     | 0.6529     |
+| cosine_recall@3     | 0.7571     |
+| cosine_recall@5     | 0.8186     |
+| cosine_recall@10    | 0.8686     |
+| cosine_ndcg@10      | 0.7557     |
+| cosine_mrr@10       | 0.7201     |
+| **cosine_map@100**  | **0.7249** |
+
+<!--
+## 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: 6 tokens</li><li>mean: 45.39 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.23 tokens</li><li>max: 45 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                              | anchor                                                                                                            |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|
+  | <code>Chubb mitigates exposure to climate change risk by ceding catastrophe risk in our insurance portfolio through both reinsurance and capital markets, and our investment portfolio through the diversification of risk, industry, location, type and duration of security.</code> | <code>How does Chubb respond to the risks associated with climate change?</code>                                  |
+  | <code>Item 8 of Part IV in the Annual Report on Form 10-K details the consolidated financial statements and accompanying notes.</code>                                                                                                                                                | <code>What documents are detailed in Item 8 of Part IV of the Annual Report on Form 10-K?</code>                  |
+  | <code>While the outcome of this matter cannot be determined at this time, it is not currently expected to have a material adverse impact on our business.</code>                                                                                                                      | <code>Is the outcome of the investigation into Tesla's waste segregation practices currently determinable?</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     | 1.521         | -                      | -                      | -                      | -                     | -                      |
+| 0.9746     | 12     | -             | 0.7434                 | 0.7579                 | 0.7641                 | 0.6994                | 0.7678                 |
+| 1.6244     | 20     | 0.6597        | -                      | -                      | -                      | -                     | -                      |
+| 1.9492     | 24     | -             | 0.7583                 | 0.7628                 | 0.7726                 | 0.7219                | 0.7735                 |
+| 2.4365     | 30     | 0.4472        | -                      | -                      | -                      | -                     | -                      |
+| 2.9239     | 36     | -             | 0.7578                 | 0.7661                 | 0.7747                 | 0.7251                | 0.7753                 |
+| 3.2487     | 40     | 0.3865        | -                      | -                      | -                      | -                     | -                      |
+| **3.8985** | **48** | **-**         | **0.7596**             | **0.7685**             | **0.7757**             | **0.7249**            | **0.7747**             |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.0
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.30.1
+- 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": "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:dcbcacd6875d719664ae5db69e0c548e003636818b4dd43e47cd408beff4ef0d
+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]"
+}