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 +1,848 @@
+---
+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:100
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 'Fig. 8. The accuracy of instruct-GPT series models of different
+    sizes (left to right, small to large). Larger model doing better on binary classification
+    of answerable and unanswerable questions in SelfAware eval. (Image source: Yin
+    et al. 2023)
+
+    Another way to assess the model’s awareness of unknown knowledge is to measure
+    the model’s output uncertainty. When a question is in-between known and unknown,
+    the model is expected to demonstrate the right level of confidence.
+
+    The experiment by Kadavath et al. (2022) showed that LLMs are shown to be well
+    calibrated in their estimation probabilities of answer correctness on diverse
+    multiple choice questions in a format with visible lettered answer options (MMLU,
+    TruthfulQA, QuALITY, LogiQA), meaning that the predicted probability coincides
+    with the frequency of that answer being true. RLHF fine-tuning makes the model
+    poorly calibrated, but higher sampling temperature leads to better calibration
+    results.'
+  sentences:
+  - What effect does the slower acquisition of new knowledge compared to established
+    knowledge have on the effectiveness of large language models in practical scenarios?
+  - How do discrepancies identified during the final output review phase affect the
+    overall quality of the generated responses?
+  - What effect does reinforcement learning from human feedback (RLHF) fine-tuning
+    have on how well large language models assess the accuracy of their answers?
+- source_sentence: 'Fig. 1. Knowledge categorization of close-book QA examples based
+    on how likely the model outputs correct answers. (Image source: Gekhman et al.
+    2024)
+
+    Some interesting observations of the experiments, where dev set accuracy is considered
+    a proxy for hallucinations.
+
+
+    Unknown examples are fitted substantially slower than Known.
+
+    The best dev performance is obtained when the LLM fits the majority of the Known
+    training examples but only a few of the Unknown ones. The model starts to hallucinate
+    when it learns most of the Unknown examples.
+
+    Among Known examples, MaybeKnown cases result in better overall performance, more
+    essential than HighlyKnown ones.'
+  sentences:
+  - What is the relationship between the structural formatting of inquiries and the
+    occurrence of calibration errors in artificial intelligence models, and in what
+    ways can this understanding contribute to the optimization of model training processes?
+  - What are the benefits of integrating a pretrained Natural Language Inference (NLI)
+    model with MPNet when assessing the reliability of reasoning paths in knowledge
+    retrieval?
+  - In what ways do the classifications of Known versus Unknown examples influence
+    the propensity of AI models to generate hallucinations during their training processes?
+- source_sentence: 'Fig. 3. The evaluation framework for the FactualityPrompt benchmark.(Image
+    source: Lee, et al. 2022)
+
+    Given the model continuation and paired Wikipedia text, two evaluation metrics
+    for hallucination are considered:
+
+
+    Hallucination NE (Named Entity) errors: Using a pretrained entity detection model
+    and document-level grounding, this metric measures the fraction of detected named
+    entities that do not appear in the ground truth document.
+
+    Entailment ratios: Using a RoBERTa model fine-tuned on MNLI and sentence-level
+    knowledge grounding, this metric calculates the fraction of generated sentences
+    that are marked as relevant to the paired Wikipedia sentence by the entailment
+    model.'
+  sentences:
+  - What impact does the implementation of a pretrained query-document relevance model
+    have on the process of document selection in research methodologies?
+  - In what ways does the sequence in which information is delivered in AI-generated
+    responses influence the likelihood of generating inaccuracies or hallucinations?
+  - In what ways does the FactualityPrompt benchmark assess the performance of named
+    entity detection models, particularly in relation to errors arising from hallucinated
+    named entities?
+- source_sentence: 'Fig. 1. Knowledge categorization of close-book QA examples based
+    on how likely the model outputs correct answers. (Image source: Gekhman et al.
+    2024)
+
+    Some interesting observations of the experiments, where dev set accuracy is considered
+    a proxy for hallucinations.
+
+
+    Unknown examples are fitted substantially slower than Known.
+
+    The best dev performance is obtained when the LLM fits the majority of the Known
+    training examples but only a few of the Unknown ones. The model starts to hallucinate
+    when it learns most of the Unknown examples.
+
+    Among Known examples, MaybeKnown cases result in better overall performance, more
+    essential than HighlyKnown ones.'
+  sentences:
+  - In what ways does the inherently adversarial structure of TruthfulQA inquiries
+    facilitate the detection of prevalent fallacies in human cognitive processes,
+    and what implications does this have for understanding the constraints of expansive
+    language models?
+  - In what ways do MaybeKnown cases influence the performance of a model when contrasted
+    with HighlyKnown examples, particularly in relation to the occurrence of hallucinations?
+  - In what ways does the Self-RAG framework leverage reflection tokens to enhance
+    the quality of its generated outputs, and what implications does this have for
+    the overall generation process?
+- source_sentence: 'Fine-tuning New Knowledge#
+
+    Fine-tuning a pre-trained LLM via supervised fine-tuning and RLHF is a common
+    technique for improving certain capabilities of the model like instruction following.
+    Introducing new knowledge at the fine-tuning stage is hard to avoid.
+
+    Fine-tuning usually consumes much less compute, making it debatable whether the
+    model can reliably learn new knowledge via small-scale fine-tuning. Gekhman et
+    al. 2024 studied the research question of whether fine-tuning LLMs on new knowledge
+    encourages hallucinations. They found that (1) LLMs learn fine-tuning examples
+    with new knowledge slower than other examples with knowledge consistent with the
+    pre-existing knowledge of the model; (2) Once the examples with new knowledge
+    are eventually learned, they increase the model’s tendency to hallucinate.'
+  sentences:
+  - How does the IsRel token function in the retrieval process, and what impact does
+    it have on the relevance of generated content to reduce hallucination?
+  - What is the relationship between the calibration of AI models and the effectiveness
+    of verbalized probabilities when applied to tasks of varying difficulty levels?
+  - How do the results presented by Gekhman et al. in their 2024 study inform our
+    understanding of the reliability metrics associated with large language models
+    (LLMs) when subjected to fine-tuning with novel datasets?
+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: 0.828125
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9635416666666666
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9739583333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9947916666666666
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.828125
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3211805555555556
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1947916666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09947916666666667
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.828125
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9635416666666666
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9739583333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9947916666666666
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9220150687007592
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8976707175925925
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8981047453703703
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8020833333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9635416666666666
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9739583333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9895833333333334
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8020833333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3211805555555556
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1947916666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09895833333333333
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8020833333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9635416666666666
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9739583333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9895833333333334
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9077325270335209
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.880220734126984
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8810414411976911
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.796875
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9583333333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.96875
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9791666666666666
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.796875
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3194444444444445
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19374999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09791666666666665
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.796875
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9583333333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.96875
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9791666666666666
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9011377823848584
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8746155753968253
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8757564484126984
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7864583333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9322916666666666
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9635416666666666
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9635416666666666
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7864583333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3107638888888889
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19270833333333334
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09635416666666667
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7864583333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9322916666666666
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9635416666666666
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9635416666666666
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.888061438431803
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8623263888888889
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8647421480429293
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6875
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8645833333333334
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9270833333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.96875
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.6875
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2881944444444445
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.18541666666666665
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09687499999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6875
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8645833333333334
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9270833333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.96875
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8335872598831777
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.7895895337301586
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.7917890681938919
+      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("joshuapb/fine-tuned-matryoshka-100")
+# Run inference
+sentences = [
+    'Fine-tuning New Knowledge#\nFine-tuning a pre-trained LLM via supervised fine-tuning and RLHF is a common technique for improving certain capabilities of the model like instruction following. Introducing new knowledge at the fine-tuning stage is hard to avoid.\nFine-tuning usually consumes much less compute, making it debatable whether the model can reliably learn new knowledge via small-scale fine-tuning. Gekhman et al. 2024 studied the research question of whether fine-tuning LLMs on new knowledge encourages hallucinations. They found that (1) LLMs learn fine-tuning examples with new knowledge slower than other examples with knowledge consistent with the pre-existing knowledge of the model; (2) Once the examples with new knowledge are eventually learned, they increase the model’s tendency to hallucinate.',
+    'How do the results presented by Gekhman et al. in their 2024 study inform our understanding of the reliability metrics associated with large language models (LLMs) when subjected to fine-tuning with novel datasets?',
+    'What is the relationship between the calibration of AI models and the effectiveness of verbalized probabilities when applied to tasks of varying difficulty levels?',
+]
+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   | 0.8281     |
+| cosine_accuracy@3   | 0.9635     |
+| cosine_accuracy@5   | 0.974      |
+| cosine_accuracy@10  | 0.9948     |
+| cosine_precision@1  | 0.8281     |
+| cosine_precision@3  | 0.3212     |
+| cosine_precision@5  | 0.1948     |
+| cosine_precision@10 | 0.0995     |
+| cosine_recall@1     | 0.8281     |
+| cosine_recall@3     | 0.9635     |
+| cosine_recall@5     | 0.974      |
+| cosine_recall@10    | 0.9948     |
+| cosine_ndcg@10      | 0.922      |
+| cosine_mrr@10       | 0.8977     |
+| **cosine_map@100**  | **0.8981** |
+
+#### 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   | 0.8021    |
+| cosine_accuracy@3   | 0.9635    |
+| cosine_accuracy@5   | 0.974     |
+| cosine_accuracy@10  | 0.9896    |
+| cosine_precision@1  | 0.8021    |
+| cosine_precision@3  | 0.3212    |
+| cosine_precision@5  | 0.1948    |
+| cosine_precision@10 | 0.099     |
+| cosine_recall@1     | 0.8021    |
+| cosine_recall@3     | 0.9635    |
+| cosine_recall@5     | 0.974     |
+| cosine_recall@10    | 0.9896    |
+| cosine_ndcg@10      | 0.9077    |
+| cosine_mrr@10       | 0.8802    |
+| **cosine_map@100**  | **0.881** |
+
+#### 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   | 0.7969     |
+| cosine_accuracy@3   | 0.9583     |
+| cosine_accuracy@5   | 0.9688     |
+| cosine_accuracy@10  | 0.9792     |
+| cosine_precision@1  | 0.7969     |
+| cosine_precision@3  | 0.3194     |
+| cosine_precision@5  | 0.1937     |
+| cosine_precision@10 | 0.0979     |
+| cosine_recall@1     | 0.7969     |
+| cosine_recall@3     | 0.9583     |
+| cosine_recall@5     | 0.9688     |
+| cosine_recall@10    | 0.9792     |
+| cosine_ndcg@10      | 0.9011     |
+| cosine_mrr@10       | 0.8746     |
+| **cosine_map@100**  | **0.8758** |
+
+#### 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   | 0.7865     |
+| cosine_accuracy@3   | 0.9323     |
+| cosine_accuracy@5   | 0.9635     |
+| cosine_accuracy@10  | 0.9635     |
+| cosine_precision@1  | 0.7865     |
+| cosine_precision@3  | 0.3108     |
+| cosine_precision@5  | 0.1927     |
+| cosine_precision@10 | 0.0964     |
+| cosine_recall@1     | 0.7865     |
+| cosine_recall@3     | 0.9323     |
+| cosine_recall@5     | 0.9635     |
+| cosine_recall@10    | 0.9635     |
+| cosine_ndcg@10      | 0.8881     |
+| cosine_mrr@10       | 0.8623     |
+| **cosine_map@100**  | **0.8647** |
+
+#### 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   | 0.6875     |
+| cosine_accuracy@3   | 0.8646     |
+| cosine_accuracy@5   | 0.9271     |
+| cosine_accuracy@10  | 0.9688     |
+| cosine_precision@1  | 0.6875     |
+| cosine_precision@3  | 0.2882     |
+| cosine_precision@5  | 0.1854     |
+| cosine_precision@10 | 0.0969     |
+| cosine_recall@1     | 0.6875     |
+| cosine_recall@3     | 0.8646     |
+| cosine_recall@5     | 0.9271     |
+| cosine_recall@10    | 0.9688     |
+| cosine_ndcg@10      | 0.8336     |
+| cosine_mrr@10       | 0.7896     |
+| **cosine_map@100**  | **0.7918** |
+
+<!--
+## 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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `load_best_model_at_end`: True
+
+#### 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`: 8
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `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`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `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
+- `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
+- `eval_on_start`: False
+- `batch_sampler`: batch_sampler
+- `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 |
+|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.3846  | 5      | 5.0472        | -                      | -                      | -                      | -                     | -                      |
+| 0.7692  | 10     | 4.0023        | -                      | -                      | -                      | -                     | -                      |
+| 1.0     | 13     | -             | 0.7939                 | 0.8135                 | 0.8282                 | 0.7207                | 0.8323                 |
+| 1.1538  | 15     | 2.3381        | -                      | -                      | -                      | -                     | -                      |
+| 1.5385  | 20     | 3.4302        | -                      | -                      | -                      | -                     | -                      |
+| 1.9231  | 25     | 2.08          | -                      | -                      | -                      | -                     | -                      |
+| 2.0     | 26     | -             | 0.8494                 | 0.8681                 | 0.8781                 | 0.7959                | 0.8888                 |
+| 2.3077  | 30     | 1.4696        | -                      | -                      | -                      | -                     | -                      |
+| 2.6923  | 35     | 1.8153        | -                      | -                      | -                      | -                     | -                      |
+| **3.0** | **39** | **-**         | **0.8641**             | **0.8844**             | **0.8924**             | **0.7952**            | **0.8997**             |
+| 3.0769  | 40     | 1.3498        | -                      | -                      | -                      | -                     | -                      |
+| 3.4615  | 45     | 0.9135        | -                      | -                      | -                      | -                     | -                      |
+| 3.8462  | 50     | 1.3996        | -                      | -                      | -                      | -                     | -                      |
+| 4.0     | 52     | -             | 0.8647                 | 0.8775                 | 0.8819                 | 0.7896                | 0.8990                 |
+| 4.2308  | 55     | 1.1582        | -                      | -                      | -                      | -                     | -                      |
+| 4.6154  | 60     | 1.2233        | -                      | -                      | -                      | -                     | -                      |
+| 5.0     | 65     | 0.9757        | 0.8647                 | 0.8758                 | 0.8810                 | 0.7918                | 0.8981                 |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.4
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.21.0
+- 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": "fine-tuned-models/fine-tuned-matryoshka-100",
+  "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": "4.42.4",
+  "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": "4.42.4",
+    "pytorch": "2.3.1+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 sha256:0b08db95f8a154f5092c7ec86da80412642758784587f17e02cfb6554512be71
+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 +1,64 @@
+{
+  "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]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}