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: Goodwill is recognized for the excess of the purchase price over
+    the fair value of tangible and identifiable intangible net assets of businesses
+    acquired. In evaluating goodwill impairment, a qualitative assessment is performed
+    to determine the likelihood that the fair value of a reporting unit is less than
+    its carrying amount. This might lead to further testing of goodwill for impairment,
+    which includes comparing the fair value of the reporting unit to its carrying
+    value (including attributable goodwill). Fair value for our reporting units is
+    determined using an income or market approach incorporating market participant
+    considerations and management’s assumptions.
+  sentences:
+  - How is goodwill reviewed for impairment in a company, and what methods are used
+    to determine the fair value of reporting units?
+  - What regulatory framework does the FCC currently apply to broadband internet access
+    services as of 2023?
+  - What were the total interest payments made by the company in 2023, 2022, and 2021?
+- source_sentence: Part IV Item 15, titled 'Exhibits, Financial Statement Schedules',
+    includes the 'Index to Financial Statements' and the 'Index to Financial Statement
+    Schedules.'
+  sentences:
+  - What was the net cash from operations reported for the year ended June 30, 2023?
+  - Where would one find the 'Index to Financial Statements' and the 'Index to Financial
+    Statement Schedules' mentioned?
+  - What is the trajectory of the AMPTC for microinverters starting in 2030?
+- source_sentence: As of December 31, 2023, the total amortized cost, net of valuation
+    allowance, for non-U.S. government securities amounted to $14,516 million.
+  sentences:
+  - How much federal net operating loss carryforwards did the company have at the
+    end of 2023, and how much of it is expected to be realized?
+  - What accounting principles are followed in the preparation of Goldman Sachs' consolidated
+    financial statements for 2023?
+  - What was the total amortized cost, net of valuation allowance, for non-U.S. government
+    securities as of December 31, 2023?
+- source_sentence: Information about legal proceedings in the Annual Report on Form
+    10-K is incorporated by reference under several notes and sections.
+  sentences:
+  - What method is used to provide information about legal proceedings in the Annual
+    Report on Form 10-K?
+  - What can cause delays in pharmaceutical product launches?
+  - What was the total amount of cash dividends declared by the company per share
+    in the fiscal year ending on October 1, 2023?
+- source_sentence: MERS database revenues contain multiple performance obligations
+    related to each new loan registration and future transfers, and the revenues are
+    primarily recorded at the point in time of each transaction.
+  sentences:
+  - How are revenues from MERS database recognized?
+  - How did The Home Depot, Inc.'s basic earnings per share change from 2020 to 2022?
+  - How many active sellers and buyers did Etsy's marketplaces connect as of December
+    31, 2023?
+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: 0.7228571428571429
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8528571428571429
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.89
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9185714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7228571428571429
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2842857142857143
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17799999999999996
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09185714285714283
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7228571428571429
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8528571428571429
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.89
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9185714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8244010006831627
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7936836734693877
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7971656786986449
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7157142857142857
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8471428571428572
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8857142857142857
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9185714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7157142857142857
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.28238095238095234
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17714285714285713
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09185714285714283
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7157142857142857
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8471428571428572
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8857142857142857
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9185714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8209116379330612
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7891343537414967
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7926472335071902
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.71
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8428571428571429
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8757142857142857
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9128571428571428
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.71
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.28095238095238095
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17514285714285713
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09128571428571428
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.71
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8428571428571429
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8757142857142857
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9128571428571428
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8138965576076403
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7818429705215417
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7855894139852542
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6871428571428572
+      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: 0.6871428571428572
+      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: 0.6871428571428572
+      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: 0.7953389524625682
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7622392290249432
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7667451557566504
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6628571428571428
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7842857142857143
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8371428571428572
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8771428571428571
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6628571428571428
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.26142857142857145
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1674285714285714
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0877142857142857
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6628571428571428
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7842857142857143
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8371428571428572
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8771428571428571
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7701231991584621
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7357777777777779
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7410692697767751
+      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("chcho/bge-base-financial-matryoshka")
+# Run inference
+sentences = [
+    'MERS database revenues contain multiple performance obligations related to each new loan registration and future transfers, and the revenues are primarily recorded at the point in time of each transaction.',
+    'How are revenues from MERS database recognized?',
+    "How many active sellers and buyers did Etsy's marketplaces connect as of December 31, 2023?",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* 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   | 0.7229     | 0.7157     | 0.71       | 0.6871     | 0.6629     |
+| cosine_accuracy@3   | 0.8529     | 0.8471     | 0.8429     | 0.8186     | 0.7843     |
+| cosine_accuracy@5   | 0.89       | 0.8857     | 0.8757     | 0.8629     | 0.8371     |
+| cosine_accuracy@10  | 0.9186     | 0.9186     | 0.9129     | 0.8971     | 0.8771     |
+| cosine_precision@1  | 0.7229     | 0.7157     | 0.71       | 0.6871     | 0.6629     |
+| cosine_precision@3  | 0.2843     | 0.2824     | 0.281      | 0.2729     | 0.2614     |
+| cosine_precision@5  | 0.178      | 0.1771     | 0.1751     | 0.1726     | 0.1674     |
+| cosine_precision@10 | 0.0919     | 0.0919     | 0.0913     | 0.0897     | 0.0877     |
+| cosine_recall@1     | 0.7229     | 0.7157     | 0.71       | 0.6871     | 0.6629     |
+| cosine_recall@3     | 0.8529     | 0.8471     | 0.8429     | 0.8186     | 0.7843     |
+| cosine_recall@5     | 0.89       | 0.8857     | 0.8757     | 0.8629     | 0.8371     |
+| cosine_recall@10    | 0.9186     | 0.9186     | 0.9129     | 0.8971     | 0.8771     |
+| **cosine_ndcg@10**  | **0.8244** | **0.8209** | **0.8139** | **0.7953** | **0.7701** |
+| cosine_mrr@10       | 0.7937     | 0.7891     | 0.7818     | 0.7622     | 0.7358     |
+| cosine_map@100      | 0.7972     | 0.7926     | 0.7856     | 0.7667     | 0.7411     |
+
+<!--
+## 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: 2 tokens</li><li>mean: 46.48 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.5 tokens</li><li>max: 51 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                          | anchor                                                                                                              |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|
+  | <code>We use a variety of methodologies to determine the fair value of these assets, including discounted cash flow models, which include assumptions we believe are consistent with those a market participant would use.</code> | <code>How is the fair value of intangible assets determined within a company?</code>                                |
+  | <code>We continue to own a 35% minority ownership in Gentiva Hospice operations after it was restructured into a new stand-alone company.</code>                                                                                  | <code>What percentage minority ownership does the company retain in Gentiva Hospice after the restructuring?</code> |
+  | <code>The net interest income for the first quarter of 2023 was $14,448 million.</code>                                                                                                                                           | <code>What was the net interest income for the first quarter of 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     | 1.5791        | -                      | -                      | -                      | -                      | -                     |
+| 0.9746     | 12     | -             | 0.8089                 | 0.8028                 | 0.7958                 | 0.7714                 | 0.7428                |
+| 1.6244     | 20     | 0.6637        | -                      | -                      | -                      | -                      | -                     |
+| 1.9492     | 24     | -             | 0.8209                 | 0.8166                 | 0.8109                 | 0.7913                 | 0.7615                |
+| 2.4365     | 30     | 0.5072        | -                      | -                      | -                      | -                      | -                     |
+| **2.9239** | **36** | **-**         | **0.8229**             | **0.82**               | **0.8133**             | **0.7959**             | **0.7704**            |
+| 3.2487     | 40     | 0.394         | -                      | -                      | -                      | -                      | -                     |
+| 3.8985     | 48     | -             | 0.8244                 | 0.8209                 | 0.8139                 | 0.7953                 | 0.7701                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.9.5
+- Sentence Transformers: 3.3.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.27.2
+- 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:de8acbcffd19065d9183107c169494fe25c1babbc8c030a6743752ecc7534d93
+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]"
+}