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
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6300
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-base-en-v1.5
+widget:
+- source_sentence: What year do the patent families related to DARZALEX expire in
+    the United States?
+  sentences:
+  - Amortization for owned content predominantly monetized on an individual basis
+    and accrued costs associated with participations and residuals payments are recorded
+    using the individual film forecast computation method, which recognizes the costs
+    in the same ratio as the associated ultimate revenue.
+  - The two patent families both expire in the United States in 2029.
+  - For the year ended December 31, 2022, net cash used in investing activities of
+    $371.9 million was primarily from the purchase of $247.3 million marketable securities,
+    net of sale and maturities, $62.2 million net cash used to acquire GreenCom, SolarLeadFactory
+    and ClipperCreek, $46.4 million used in purchases of test and assembly equipment
+    to expand our supply capacity, related facility improvements and information technology
+    enhancements, including capitalized costs related to internal-use software and
+    $16.0 million used to invest in private companies.
+- source_sentence: What legal claims does Fortis Advisors LLC allege against Ethicon
+    Inc. in the lawsuit related to the acquisition of Auris Health Inc.?
+  sentences:
+  - Payments include a single lump-sum per treatment, referred to as bundled rates,
+    or in other cases separate payments for dialysis treatments and pharmaceuticals,
+    referred to as FFS rates.
+  - In October 2020, Fortis Advisors LLC filed a complaint against Ethicon Inc. and
+    others in Delaware's Court of Chancery. The lawsuit alleges breach of contract
+    and fraud related to Ethicon's acquisition of Auris Health Inc. in 2019. The case
+    underwent a partial dismissal in December 2021, and as of January 2024, the trial's
+    decision is pending.
+  - On September 5, 2023, ICE acquired 100% of Black Knight for aggregate transaction
+    consideration of approximately $11.8 billion, or $76 per share of Black Knight
+    common stock, with cash comprising 90% of the value of the aggregate transaction
+    consideration. The aggregate cash component of the transaction consideration was
+    $10.5 billion. ICE issued 10.9 million shares of its common stock to Black Knight
+    stockholders, which was based on the market price of the common stock and the
+    average of the volume weighted averages of the trading prices of the common stock
+    on each of the ten consecutive trading days ending three trading days prior to
+    the closing of the merger.
+- source_sentence: What caused the increase in net cash provided by operating activities
+    between 2022 and 2023?
+  sentences:
+  - Net cash provided by operating activities was $712.2 million and $223.7 million
+    for the year ended December 31, 2023 and 2022, respectively. The increase was
+    primarily driven by timing of payments to vendors and timing of the receipt of
+    payments from our customers, as well as an increase in interest income.
+  - Joanne D. Smith held the position of Vice President - Marketing at Delta from
+    November 2005 to February 2007.
+  - Experienced management team with a proven track in the gaming and resort industry.
+    Mr. Robert G. Goldstein, our Chairman and Chief Executive Officer, has been an
+    integral part of our executive team from the beginning, joining our founder and
+    previous Chairman and Chief Executive Officer, Mr. Sheldon G. Adelson, before
+    The Venetian Resort Las Vegas was constructed. Mr. Goldstein is one of the most
+    respected and experienced executives in our industry today.
+- source_sentence: What does the company believe adds significant value to its business
+    regarding intellectual property?
+  sentences:
+  - In 2022, the net interest expense on pre-acquisition-related debt was $59 million
+    and additional adjustments included costs of $30 million associated with the May
+    and June 2022 extinguishment of four series of senior notes.
+  - Fluctuations in foreign currency exchange rates decreased our consolidated net
+    operating revenues by 4%.
+  - We believe that, to varying degrees, our trademarks, trade names, copyrights,
+    proprietary processes, trade secrets, trade dress, domain names and similar intellectual
+    property add significant value to our business
+- source_sentence: What does it mean for financial statements to be incorporated by
+    reference?
+  sentences:
+  - The consolidated financial statements are incorporated by reference in the Annual
+    Report on Form 10-K, indicating they are treated as part of the document for legal
+    and reporting purposes.
+  - The Consolidated Financial Statements, together with the Notes thereto and the
+    report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s
+    independent registered public accounting firm (PCAOB ID 238), appear on pages
+    163–309.
+  - 'The Goldman Sachs Group, Inc. manages and reports its activities in three business
+    segments: Global Banking & Markets, Asset & Wealth Samantha Management and Platform
+    Solutions.'
+pipeline_tag: sentence-similarity
+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
+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: 0.7
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8285714285714286
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8728571428571429
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9071428571428571
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2761904761904762
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17457142857142854
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09071428571428569
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8285714285714286
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8728571428571429
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9071428571428571
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8045805359515339
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7714971655328795
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.775178941729297
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7014285714285714
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.83
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8671428571428571
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9042857142857142
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7014285714285714
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27666666666666667
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1734285714285714
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09042857142857141
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7014285714285714
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.83
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8671428571428571
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9042857142857142
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8036464537429646
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.771175736961451
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7751075563277001
+      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: 0.8185714285714286
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8628571428571429
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8971428571428571
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6928571428571428
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27285714285714285
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17257142857142854
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0897142857142857
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6928571428571428
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8185714285714286
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8628571428571429
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8971428571428571
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7963364154792727
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7638741496598634
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7683107318753077
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6771428571428572
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8142857142857143
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8514285714285714
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8885714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6771428571428572
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2714285714285714
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17028571428571426
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08885714285714284
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6771428571428572
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8142857142857143
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8514285714285714
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8885714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.786332288682679
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7531507936507934
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7576033800206036
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6571428571428571
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7814285714285715
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8171428571428572
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.86
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6571428571428571
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2604761904761905
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16342857142857142
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08599999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6571428571428571
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7814285714285715
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8171428571428572
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.86
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7602042820067257
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7281371882086165
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7334805218687248
+      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) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [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 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+- **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("Fe2x/bge-base-financial-matryoshka")
+# Run inference
+sentences = [
+    'What does it mean for financial statements to be incorporated by reference?',
+    'The consolidated financial statements are incorporated by reference in the Annual Report on Form 10-K, indicating they are treated as part of the document for legal and reporting purposes.',
+    'The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s independent registered public accounting firm (PCAOB ID 238), appear on pages 163–309.',
+]
+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
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.7        | 0.7014     | 0.6929     | 0.6771     | 0.6571     |
+| cosine_accuracy@3   | 0.8286     | 0.83       | 0.8186     | 0.8143     | 0.7814     |
+| cosine_accuracy@5   | 0.8729     | 0.8671     | 0.8629     | 0.8514     | 0.8171     |
+| cosine_accuracy@10  | 0.9071     | 0.9043     | 0.8971     | 0.8886     | 0.86       |
+| cosine_precision@1  | 0.7        | 0.7014     | 0.6929     | 0.6771     | 0.6571     |
+| cosine_precision@3  | 0.2762     | 0.2767     | 0.2729     | 0.2714     | 0.2605     |
+| cosine_precision@5  | 0.1746     | 0.1734     | 0.1726     | 0.1703     | 0.1634     |
+| cosine_precision@10 | 0.0907     | 0.0904     | 0.0897     | 0.0889     | 0.086      |
+| cosine_recall@1     | 0.7        | 0.7014     | 0.6929     | 0.6771     | 0.6571     |
+| cosine_recall@3     | 0.8286     | 0.83       | 0.8186     | 0.8143     | 0.7814     |
+| cosine_recall@5     | 0.8729     | 0.8671     | 0.8629     | 0.8514     | 0.8171     |
+| cosine_recall@10    | 0.9071     | 0.9043     | 0.8971     | 0.8886     | 0.86       |
+| **cosine_ndcg@10**  | **0.8046** | **0.8036** | **0.7963** | **0.7863** | **0.7602** |
+| cosine_mrr@10       | 0.7715     | 0.7712     | 0.7639     | 0.7532     | 0.7281     |
+| cosine_map@100      | 0.7752     | 0.7751     | 0.7683     | 0.7576     | 0.7335     |
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 6,300 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 20.44 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 45.16 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                             | positive                                                                                                                                                                                                                                                                                                                                      |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What was the amount of cash generated from operations by the company in fiscal year 2023?</code>                                             | <code>Highlights during fiscal year 2023 include the following: We generated $18,085 million of cash from operations.</code>                                                                                                                                                                                                                  |
+  | <code>How much were unrealized losses on U.S. government and agency securities for those held for 12 months or greater as of June 30, 2023?</code> | <code>U.S. government and agency securities | $ | 7,950 | | $ | (336 | ) | $ | 45,273 | $ | (3,534 | ) | $ | 53,223 | $ | (3,870 | )</code>                                                                                                                                                                                                   |
+  | <code>How is the impairment of assets assessed for projects still under development?</code>                                                        | <code>For assets under development, assets are grouped and assessed for impairment by estimating the undiscounted cash flows, which include remaining construction costs, over the asset's remaining useful life. If cash flows do not exceed the carrying amount, impairment based on fair value versus carrying value is considered.</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
+- `fp16`: True
+- `tf32`: False
+- `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
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 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`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: False
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `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
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch     | Step   | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.8122    | 10     | 1.5872        | -                      | -                      | -                      | -                      | -                     |
+| 1.0       | 13     | -             | 0.7879                 | 0.7860                 | 0.7782                 | 0.7698                 | 0.7320                |
+| 1.5685    | 20     | 0.6329        | -                      | -                      | -                      | -                      | -                     |
+| 2.0       | 26     | -             | 0.7988                 | 0.7969                 | 0.7923                 | 0.7826                 | 0.7520                |
+| 2.3249    | 30     | 0.4465        | -                      | -                      | -                      | -                      | -                     |
+| 3.0       | 39     | -             | 0.8046                 | 0.8026                 | 0.7959                 | 0.7855                 | 0.7596                |
+| 3.0812    | 40     | 0.349         | -                      | -                      | -                      | -                      | -                     |
+| **3.731** | **48** | **-**         | **0.8046**             | **0.8036**             | **0.7963**             | **0.7863**             | **0.7602**            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.9.20
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.1.2+cu121
+- Accelerate: 1.2.1
+- Datasets: 2.19.1
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### 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": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72b540dbcfd6a79edba1110d200d199c801293f597ac76f5207a05b1eee1f0a2
+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 +1,58 @@
+{
+  "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,
+  "extra_special_tokens": {},
+  "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]"
+}