Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,809 @@
+---
+base_model: BAAI/bge-base-en-v1.5
+datasets: []
+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 in IBM's 2023 Annual Report to Stockholders details the
+    Financial Statements and Supplementary Data, which are included on pages 44 through
+    121.
+  sentences:
+  - What was the amount gained from the disposal of assets in 2022?
+  - What section of IBM's Annual Report for 2023 contains the Financial Statements
+    and Supplementary Data?
+  - What were the cash outflows for capital expenditures in 2023 and 2022 respectively?
+- source_sentence: For the fiscal year ended March 31, 2023, Electronic Arts reported
+    a gross margin of 75.9 percent, an increase of 2.5 percentage points from the
+    previous year.
+  sentences:
+  - How did investment banking revenues at Goldman Sachs change in 2023 compared to
+    2022, and what factors contributed to this change?
+  - What was the gross margin percentage for Electronic Arts in the fiscal year ending
+    March 31, 2023?
+  - What were the risk-free interest rates for the fiscal years 2021, 2022, and 2023?
+- source_sentence: Cash, cash equivalents, and restricted cash at the beginning of
+    the period totaled $7,013 for a company.
+  sentences:
+  - What was the amount of cash, cash equivalents, and restricted cash at the beginning
+    of the period for the company?
+  - What is the impact of the new $1.25 price point on Dollar Tree’s sales units and
+    profitability?
+  - What was the total amount attributed to Goodwill in the acquisition of Nuance
+    Communications, Inc. as reported by the company?
+- source_sentence: generate our mall revenue primarily from leases with tenants through
+    base minimum rents, overage rents and reimbursements for common area maintenance
+    (CAM) and other expenditures.
+  sentences:
+  - How does Visa facilitate financial inclusion with their prepaid cards?
+  - What are the main objectives of the economic sanctions imposed by the United States
+    and other international bodies?
+  - What revenue sources does Shoppes at Venetian primarily rely on from its tenants?
+- source_sentence: For the fiscal year ended August 26, 2023, we reported net sales
+    of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022,
+    a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic
+    same store sales increase of 3.4% and net sales of $327.8 million from new domestic
+    and international stores.
+  sentences:
+  - What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared
+    to fiscal 2022?
+  - What percentage of HP's external U.S. hires in fiscal year 2023 were racially
+    or ethnically diverse?
+  - How much did GameStop Corp's valuation allowances increase during fiscal 2022?
+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: 0.6985714285714286
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8271428571428572
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8628571428571429
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8985714285714286
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6985714285714286
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2757142857142857
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17257142857142854
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08985714285714284
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6985714285714286
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8271428571428572
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8628571428571429
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8985714285714286
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8023663256793517
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7712675736961451
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7758522351159084
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.69
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8271428571428572
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.86
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9028571428571428
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.69
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2757142857142857
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17199999999999996
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09028571428571427
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.69
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8271428571428572
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.86
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9028571428571428
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7998655910794988
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7665912698412698
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7706925401671437
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6957142857142857
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8228571428571428
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.86
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8914285714285715
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6957142857142857
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2742857142857143
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17199999999999996
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08914285714285713
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6957142857142857
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8228571428571428
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.86
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8914285714285715
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7974564108711016
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7669535147392289
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7718155211819018
+      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: 0.8128571428571428
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8457142857142858
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8857142857142857
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6871428571428572
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.27095238095238094
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16914285714285712
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08857142857142856
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6871428571428572
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8128571428571428
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8457142857142858
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8857142857142857
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.787697533881839
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.756192743764172
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7610331995977764
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6328571428571429
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7771428571428571
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8171428571428572
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8571428571428571
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6328571428571429
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.259047619047619
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16342857142857142
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08571428571428569
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6328571428571429
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7771428571428571
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8171428571428572
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8571428571428571
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7482728321357093
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7131224489795914
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7189753431460272
+      name: Cosine Map@100
+---
+
+# BGE base Financial Matryoshka
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("NickyNicky/bge-base-financial-matryoshka")
+# Run inference
+sentences = [
+    'For the fiscal year ended August 26, 2023, we reported net sales of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022, a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic same store sales increase of 3.4% and net sales of $327.8 million from new domestic and international stores.',
+    "What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared to fiscal 2022?",
+    "What percentage of HP's external U.S. hires in fiscal year 2023 were racially or ethnically diverse?",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `dim_768`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.6986     |
+| cosine_accuracy@3   | 0.8271     |
+| cosine_accuracy@5   | 0.8629     |
+| cosine_accuracy@10  | 0.8986     |
+| cosine_precision@1  | 0.6986     |
+| cosine_precision@3  | 0.2757     |
+| cosine_precision@5  | 0.1726     |
+| cosine_precision@10 | 0.0899     |
+| cosine_recall@1     | 0.6986     |
+| cosine_recall@3     | 0.8271     |
+| cosine_recall@5     | 0.8629     |
+| cosine_recall@10    | 0.8986     |
+| cosine_ndcg@10      | 0.8024     |
+| cosine_mrr@10       | 0.7713     |
+| **cosine_map@100**  | **0.7759** |
+
+#### Information Retrieval
+* Dataset: `dim_512`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.69       |
+| cosine_accuracy@3   | 0.8271     |
+| cosine_accuracy@5   | 0.86       |
+| cosine_accuracy@10  | 0.9029     |
+| cosine_precision@1  | 0.69       |
+| cosine_precision@3  | 0.2757     |
+| cosine_precision@5  | 0.172      |
+| cosine_precision@10 | 0.0903     |
+| cosine_recall@1     | 0.69       |
+| cosine_recall@3     | 0.8271     |
+| cosine_recall@5     | 0.86       |
+| cosine_recall@10    | 0.9029     |
+| cosine_ndcg@10      | 0.7999     |
+| cosine_mrr@10       | 0.7666     |
+| **cosine_map@100**  | **0.7707** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.6957     |
+| cosine_accuracy@3   | 0.8229     |
+| cosine_accuracy@5   | 0.86       |
+| cosine_accuracy@10  | 0.8914     |
+| cosine_precision@1  | 0.6957     |
+| cosine_precision@3  | 0.2743     |
+| cosine_precision@5  | 0.172      |
+| cosine_precision@10 | 0.0891     |
+| cosine_recall@1     | 0.6957     |
+| cosine_recall@3     | 0.8229     |
+| cosine_recall@5     | 0.86       |
+| cosine_recall@10    | 0.8914     |
+| cosine_ndcg@10      | 0.7975     |
+| cosine_mrr@10       | 0.767      |
+| **cosine_map@100**  | **0.7718** |
+
+#### Information Retrieval
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.6871    |
+| cosine_accuracy@3   | 0.8129    |
+| cosine_accuracy@5   | 0.8457    |
+| cosine_accuracy@10  | 0.8857    |
+| cosine_precision@1  | 0.6871    |
+| cosine_precision@3  | 0.271     |
+| cosine_precision@5  | 0.1691    |
+| cosine_precision@10 | 0.0886    |
+| cosine_recall@1     | 0.6871    |
+| cosine_recall@3     | 0.8129    |
+| cosine_recall@5     | 0.8457    |
+| cosine_recall@10    | 0.8857    |
+| cosine_ndcg@10      | 0.7877    |
+| cosine_mrr@10       | 0.7562    |
+| **cosine_map@100**  | **0.761** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.6329    |
+| cosine_accuracy@3   | 0.7771    |
+| cosine_accuracy@5   | 0.8171    |
+| cosine_accuracy@10  | 0.8571    |
+| cosine_precision@1  | 0.6329    |
+| cosine_precision@3  | 0.259     |
+| cosine_precision@5  | 0.1634    |
+| cosine_precision@10 | 0.0857    |
+| cosine_recall@1     | 0.6329    |
+| cosine_recall@3     | 0.7771    |
+| cosine_recall@5     | 0.8171    |
+| cosine_recall@10    | 0.8571    |
+| cosine_ndcg@10      | 0.7483    |
+| cosine_mrr@10       | 0.7131    |
+| **cosine_map@100**  | **0.719** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 6,300 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                           | anchor                                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 46.19 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.39 tokens</li><li>max: 46 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                           | anchor                                                                                                                                   |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Cash used in financing activities in fiscal 2022 was primarily attributable to settlement of stock-based awards.</code>                                                                                                                      | <code>Why was there a net outflow of cash in financing activities in fiscal 2022?</code>                                                 |
+  | <code>Certain vendors have been impacted by volatility in the supply chain financing market.</code>                                                                                                                                                | <code>How have certain vendors been impacted in the supply chain financing market?</code>                                                |
+  | <code>In the consolidated financial statements for Visa, the net cash provided by operating activities amounted to 20,755 units in the most recent period, 18,849 units in the previous period, and 15,227 units in the period before that.</code> | <code>How much net cash did Visa's operating activities generate in the most recent period according to the financial statements?</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
+- `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`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
+|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.8122 | 10   | 1.5643        | -                      | -                      | -                      | -                     | -                      |
+| 0.9746 | 12   | -             | 0.7349                 | 0.7494                 | 0.7524                 | 0.6987                | 0.7569                 |
+| 1.6244 | 20   | 0.6756        | -                      | -                      | -                      | -                     | -                      |
+| 1.9492 | 24   | -             | 0.7555                 | 0.7659                 | 0.7683                 | 0.7190                | 0.7700                 |
+| 2.4365 | 30   | 0.4561        | -                      | -                      | -                      | -                     | -                      |
+| 2.9239 | 36   | -             | 0.7592                 | 0.7698                 | 0.7698                 | 0.7184                | 0.7741                 |
+| 3.2487 | 40   | 0.3645        | -                      | -                      | -                      | -                     | -                      |
+| 3.8985 | 48   | -             | 0.7610                 | 0.7718                 | 0.7707                 | 0.7190                | 0.7759                 |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.2.0+cu121
+- Accelerate: 0.31.0
+- 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": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.2.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18adc92675356d350ed51b2bb43686de3c95e72c6bf852ea3f443793dddb7315
+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]"
+}