Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: BAAI/bge-base-en-v1.5
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:6300
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Item 8 includes Financial Statements and Supplementary Data.
+  sentences:
+  - What does the FDA label update for Yescarta include as of the latest approval?
+  - What information can be found in Item 8 of a document?
+  - When does the Company's fiscal year end?
+- source_sentence: Item 8 in a financial document is designated for Financial Statements
+    and Supplementary Data.
+  sentences:
+  - What are the primary goals of AutoZone's store management system?
+  - What information is contained in Item 8 of a financial document?
+  - What were the pre-tax earnings of the manufacturing sector in 2023, 2022, and
+    2021?
+- source_sentence: of approximately $1.0 billion in IBNR liabilities, producing a
+    corresponding decrease in pre-tax earnings. We believe it is reasonably possible
+    for these assumptions to increase at these rates.
+  sentences:
+  - What was the decrease in pre-tax earnings due to the $1.0 billion in IBNR liabilities?
+  - What was the total long-term debt, including the current portion, for AbbVie as
+    of December 31, 2023?
+  - What feature dedicated AI hardware in an x86 processor and uses the XDNA architecture?
+- source_sentence: In the year ended December 31, 2023, sellers generated GMS of $13.2
+    billion, approximately 68% of which came from purchases made on mobile devices.
+  sentences:
+  - What was the change in the total balance of revolving credits from December 31,
+    2022, to December 31, 2023?
+  - What are the purposes of borrowings under the 2021 credit facility?
+  - What percentage of Etsy's Gross Merchandise Sales (GMS) in 2023 came from mobile
+    purchases?
+- source_sentence: As of December 31, 2023, approximately $1.80 billion is available
+    to be repatriated from Mainland China to the U.S.
+  sentences:
+  - What is the total amount of unrestricted cash available for repatriation from
+    Mainland China to the U.S. as of the end of 2023?
+  - What is the focus of the company's research and development efforts?
+  - Where does the Report of Independent Registered Public Accounting Firm begin in
+    this report?
+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: 0.6771428571428572
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8142857142857143
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8642857142857143
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9142857142857143
+      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: 0.17285714285714282
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09142857142857141
+      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: 0.8642857142857143
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9142857142857143
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7948920706768223
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7568055555555551
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7601580985784901
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6714285714285714
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8157142857142857
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8657142857142858
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.92
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6714285714285714
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27190476190476187
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17314285714285713
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09199999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6714285714285714
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8157142857142857
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8657142857142858
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.92
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7936366054643341
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7534455782312921
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.756388193211117
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6714285714285714
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8157142857142857
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8585714285714285
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9157142857142857
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6714285714285714
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27190476190476187
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1717142857142857
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09157142857142857
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6714285714285714
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8157142857142857
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8585714285714285
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9157142857142857
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7926136922070053
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7535062358276641
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7564593466816174
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6614285714285715
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8414285714285714
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8885714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6614285714285715
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.26666666666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16828571428571426
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08885714285714286
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6614285714285715
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8414285714285714
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8885714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7767052058983972
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7407840136054418
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7454236920389576
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6357142857142857
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7742857142857142
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8185714285714286
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8642857142857143
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6357142857142857
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2580952380952381
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1637142857142857
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08642857142857142
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6357142857142857
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7742857142857142
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8185714285714286
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8642857142857143
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7511926722277801
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7148713151927435
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7199017346952273
+      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("SMARTICT/bge-base-financial-matryoshka")
+# Run inference
+sentences = [
+    'As of December 31, 2023, approximately $1.80 billion is available to be repatriated from Mainland China to the U.S.',
+    'What is the total amount of unrestricted cash available for repatriation from Mainland China to the U.S. as of the end of 2023?',
+    "What is the focus of the company's research and development efforts?",
+]
+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   | 0.6771     | 0.6714     | 0.6714     | 0.6614     | 0.6357     |
+| cosine_accuracy@3   | 0.8143     | 0.8157     | 0.8157     | 0.8        | 0.7743     |
+| cosine_accuracy@5   | 0.8643     | 0.8657     | 0.8586     | 0.8414     | 0.8186     |
+| cosine_accuracy@10  | 0.9143     | 0.92       | 0.9157     | 0.8886     | 0.8643     |
+| cosine_precision@1  | 0.6771     | 0.6714     | 0.6714     | 0.6614     | 0.6357     |
+| cosine_precision@3  | 0.2714     | 0.2719     | 0.2719     | 0.2667     | 0.2581     |
+| cosine_precision@5  | 0.1729     | 0.1731     | 0.1717     | 0.1683     | 0.1637     |
+| cosine_precision@10 | 0.0914     | 0.092      | 0.0916     | 0.0889     | 0.0864     |
+| cosine_recall@1     | 0.6771     | 0.6714     | 0.6714     | 0.6614     | 0.6357     |
+| cosine_recall@3     | 0.8143     | 0.8157     | 0.8157     | 0.8        | 0.7743     |
+| cosine_recall@5     | 0.8643     | 0.8657     | 0.8586     | 0.8414     | 0.8186     |
+| cosine_recall@10    | 0.9143     | 0.92       | 0.9157     | 0.8886     | 0.8643     |
+| **cosine_ndcg@10**  | **0.7949** | **0.7936** | **0.7926** | **0.7767** | **0.7512** |
+| cosine_mrr@10       | 0.7568     | 0.7534     | 0.7535     | 0.7408     | 0.7149     |
+| cosine_map@100      | 0.7602     | 0.7564     | 0.7565     | 0.7454     | 0.7199     |
+
+<!--
+## 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>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                           | anchor                                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 46.71 tokens</li><li>max: 281 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.48 tokens</li><li>max: 43 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                         | anchor                                                                                                                                       |
+  |:---------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Information on legal proceedings is included in Note 15 to the Consolidated Financial Statements.</code>                   | <code>What note in the Consolidated Financial Statements provides details on legal proceedings?</code>                                       |
+  | <code>As of December 31, 2023, approximately $1.80 billion is available to be repatriated from Mainland China to the U.S.</code> | <code>What is the total amount of unrestricted cash available for repatriation from Mainland China to the U.S. as of the end of 2023?</code> |
+  | <code>Bank deposits amounted to $289,953 million as of December 31, 2023.</code>                                                 | <code>What was the balance of bank deposits at Charles Schwab Corporation as of December 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
+- `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     | 1.5517        | -                      | -                      | -                      | -                      | -                     |
+| 0.9746     | 12     | -             | 0.7830                 | 0.7842                 | 0.7814                 | 0.7623                 | 0.7215                |
+| 1.6244     | 20     | 0.6616        | -                      | -                      | -                      | -                      | -                     |
+| 1.9492     | 24     | -             | 0.7918                 | 0.7924                 | 0.7884                 | 0.7737                 | 0.7429                |
+| 2.4365     | 30     | 0.46          | -                      | -                      | -                      | -                      | -                     |
+| 2.9239     | 36     | -             | 0.7941                 | 0.7920                 | 0.7930                 | 0.7764                 | 0.7482                |
+| 3.2487     | 40     | 0.3917        | -                      | -                      | -                      | -                      | -                     |
+| **3.8985** | **48** | **-**         | **0.7949**             | **0.7936**             | **0.7926**             | **0.7767**             | **0.7512**            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.34.2
+- 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": "4.41.2",
+  "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": "4.41.2",
+    "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 sha256:17e5ff4be59fe68b9e012dc083fe5b7810c7623dd937dbfd203680fd908b1954
+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]"
+}