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 +1,925 @@
+---
+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:500
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 'Non-context LLM: Prompt LLM directly with <atomic-fact> True or
+    False? without additional context.
+
+    Retrieval→LLM: Prompt with $k$ related passages retrieved from the knowledge source
+    as context.
+
+    Nonparametric probability (NP)): Compute the average likelihood of tokens in the
+    atomic fact by a masked LM and use that to make a prediction.
+
+    Retrieval→LLM + NP: Ensemble of two methods.
+
+
+    Some interesting observations on model hallucination behavior:
+
+
+    Error rates are higher for rarer entities in the task of biography generation.
+
+    Error rates are higher for facts mentioned later in the generation.
+
+    Using retrieval to ground the model generation significantly helps reduce hallucination.'
+  sentences:
+  - What is the impact of infrequent entities on the efficacy of language models in
+    the context of biography generation?
+  - In what ways does FActScore enhance the assessment of factual accuracy in long-form
+    content generation when compared to conventional evaluation techniques?
+  - What approaches does SelfCheckGPT implement when faced with questions it cannot
+    answer, and how does this influence its overall reliability in delivering accurate
+    information?
+- source_sentence: 'Revision stage: Edit the output to correct content unsupported
+    by evidence while preserving the original content as much as possible. Initialize
+    the revised text $y=x$.
+
+
+    (1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y,
+    q, e) \to {0,1}$) checks whether the evidence $e_i$ disagrees with the current
+    revised text $y$.
+
+    (2) Only if a disagreement is detect, the edit model (via few-shot prompting +
+    CoT, $(y, q, e) \to \text{ new }y$) outputs a new version of $y$ that aims to
+    agree with evidence $e_{ij}$ while otherwise minimally altering $y$.
+
+    (3) Finally only a limited number $M=5$ of evidence goes into the attribution
+    report $A$.
+
+
+
+
+
+    Fig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision).
+    (Image source: Gao et al. 2022)
+
+    When evaluating the revised text $y$, both attribution and preservation metrics
+    matter.'
+  sentences:
+  - What impact does adjusting the sampling temperature have on the calibration of
+    large language models, and how does this influence the uncertainty of their outputs?
+  - How do unanswerable questions differ from answerable ones in the context of a
+    language model's understanding of its own capabilities?
+  - In what ways does the agreement model evaluate discrepancies between the provided
+    evidence and the updated text, and how does this evaluation impact the reliability
+    of AI-generated content modifications?
+- source_sentence: 'Non-context LLM: Prompt LLM directly with <atomic-fact> True or
+    False? without additional context.
+
+    Retrieval→LLM: Prompt with $k$ related passages retrieved from the knowledge source
+    as context.
+
+    Nonparametric probability (NP)): Compute the average likelihood of tokens in the
+    atomic fact by a masked LM and use that to make a prediction.
+
+    Retrieval→LLM + NP: Ensemble of two methods.
+
+
+    Some interesting observations on model hallucination behavior:
+
+
+    Error rates are higher for rarer entities in the task of biography generation.
+
+    Error rates are higher for facts mentioned later in the generation.
+
+    Using retrieval to ground the model generation significantly helps reduce hallucination.'
+  sentences:
+  - In what ways can the acknowledgment of uncertainty by large language models (LLMs)
+    contribute to the mitigation of hallucinations and enhance the overall factual
+    accuracy of generated content?
+  - In what ways does the process of retrieving related passages contribute to minimizing
+    hallucinations in the outputs generated by language models, and how does this
+    approach differ from the application of nonparametric probability methods?
+  - How does the triplet structure $(c, y, y^*)$ play a crucial role in the categorization
+    of errors, and in what ways does it enhance the training process of the editor
+    model?
+- 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 do the intentionally designed questions in TruthfulQA highlight prevalent
+    misunderstandings regarding AI responses in the healthcare domain?
+  - 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 the RARR methodology and the FAVA model compare in their approaches to
+    mitigating hallucination errors in generated outputs, and what key distinctions
+    can be identified between the two?
+- source_sentence: 'Revision stage: Edit the output to correct content unsupported
+    by evidence while preserving the original content as much as possible. Initialize
+    the revised text $y=x$.
+
+
+    (1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y,
+    q, e) \to {0,1}$) checks whether the evidence $e_i$ disagrees with the current
+    revised text $y$.
+
+    (2) Only if a disagreement is detect, the edit model (via few-shot prompting +
+    CoT, $(y, q, e) \to \text{ new }y$) outputs a new version of $y$ that aims to
+    agree with evidence $e_{ij}$ while otherwise minimally altering $y$.
+
+    (3) Finally only a limited number $M=5$ of evidence goes into the attribution
+    report $A$.
+
+
+
+
+
+    Fig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision).
+    (Image source: Gao et al. 2022)
+
+    When evaluating the revised text $y$, both attribution and preservation metrics
+    matter.'
+  sentences:
+  - What mechanisms does the editing algorithm employ to maintain fidelity to the
+    source material while simultaneously ensuring alignment with the supporting evidence?
+  - What is the impact of constraining the dataset to a maximum of $M=5$ instances
+    on the accuracy and reliability of the attribution report $A$ when analyzing AI-generated
+    content?
+  - In what ways does the implementation of a query generation model enhance the research
+    phase when it comes to validating the accuracy of information?
+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: 0.8802083333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.96875
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9895833333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8802083333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3229166666666667
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19791666666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8802083333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.96875
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9895833333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9477255159324969
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9301711309523809
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.930171130952381
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.875
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.96875
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9947916666666666
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.875
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3229166666666667
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19895833333333335
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.875
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.96875
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9947916666666666
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9459628876705072
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9277405753968253
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9277405753968253
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8802083333333334
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.96875
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9947916666666666
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8802083333333334
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3229166666666667
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19895833333333335
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8802083333333334
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.96875
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9947916666666666
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9458393511377685
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9277405753968254
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9277405753968253
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8697916666666666
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.984375
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9895833333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9947916666666666
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8697916666666666
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.328125
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19791666666666666
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09947916666666667
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8697916666666666
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.984375
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9895833333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9947916666666666
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9440191417149189
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9265252976190478
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.92687251984127
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8541666666666666
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.984375
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9947916666666666
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9947916666666666
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8541666666666666
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.328125
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19895833333333335
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09947916666666667
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8541666666666666
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.984375
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9947916666666666
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9947916666666666
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9380774892768095
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9184027777777778
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9186111111111112
+      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-500")
+# Run inference
+sentences = [
+    'Revision stage: Edit the output to correct content unsupported by evidence while preserving the original content as much as possible. Initialize the revised text $y=x$.\n\n(1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y, q, e) \\to {0,1}$) checks whether the evidence $e_i$ disagrees with the current revised text $y$.\n(2) Only if a disagreement is detect, the edit model (via few-shot prompting + CoT, $(y, q, e) \\to \\text{ new }y$) outputs a new version of $y$ that aims to agree with evidence $e_{ij}$ while otherwise minimally altering $y$.\n(3) Finally only a limited number $M=5$ of evidence goes into the attribution report $A$.\n\n\n\n\nFig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision). (Image source: Gao et al. 2022)\nWhen evaluating the revised text $y$, both attribution and preservation metrics matter.',
+    'What mechanisms does the editing algorithm employ to maintain fidelity to the source material while simultaneously ensuring alignment with the supporting evidence?',
+    'What is the impact of constraining the dataset to a maximum of $M=5$ instances on the accuracy and reliability of the attribution report $A$ when analyzing AI-generated content?',
+]
+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   | 0.8802     |
+| cosine_accuracy@3   | 0.9688     |
+| cosine_accuracy@5   | 0.9896     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8802     |
+| cosine_precision@3  | 0.3229     |
+| cosine_precision@5  | 0.1979     |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8802     |
+| cosine_recall@3     | 0.9688     |
+| cosine_recall@5     | 0.9896     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9477     |
+| cosine_mrr@10       | 0.9302     |
+| **cosine_map@100**  | **0.9302** |
+
+#### 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   | 0.875      |
+| cosine_accuracy@3   | 0.9688     |
+| cosine_accuracy@5   | 0.9948     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.875      |
+| cosine_precision@3  | 0.3229     |
+| cosine_precision@5  | 0.199      |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.875      |
+| cosine_recall@3     | 0.9688     |
+| cosine_recall@5     | 0.9948     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.946      |
+| cosine_mrr@10       | 0.9277     |
+| **cosine_map@100**  | **0.9277** |
+
+#### 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   | 0.8802     |
+| cosine_accuracy@3   | 0.9688     |
+| cosine_accuracy@5   | 0.9948     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8802     |
+| cosine_precision@3  | 0.3229     |
+| cosine_precision@5  | 0.199      |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8802     |
+| cosine_recall@3     | 0.9688     |
+| cosine_recall@5     | 0.9948     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9458     |
+| cosine_mrr@10       | 0.9277     |
+| **cosine_map@100**  | **0.9277** |
+
+#### 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   | 0.8698     |
+| cosine_accuracy@3   | 0.9844     |
+| cosine_accuracy@5   | 0.9896     |
+| cosine_accuracy@10  | 0.9948     |
+| cosine_precision@1  | 0.8698     |
+| cosine_precision@3  | 0.3281     |
+| cosine_precision@5  | 0.1979     |
+| cosine_precision@10 | 0.0995     |
+| cosine_recall@1     | 0.8698     |
+| cosine_recall@3     | 0.9844     |
+| cosine_recall@5     | 0.9896     |
+| cosine_recall@10    | 0.9948     |
+| cosine_ndcg@10      | 0.944      |
+| cosine_mrr@10       | 0.9265     |
+| **cosine_map@100**  | **0.9269** |
+
+#### 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   | 0.8542     |
+| cosine_accuracy@3   | 0.9844     |
+| cosine_accuracy@5   | 0.9948     |
+| cosine_accuracy@10  | 0.9948     |
+| cosine_precision@1  | 0.8542     |
+| cosine_precision@3  | 0.3281     |
+| cosine_precision@5  | 0.199      |
+| cosine_precision@10 | 0.0995     |
+| cosine_recall@1     | 0.8542     |
+| cosine_recall@3     | 0.9844     |
+| cosine_recall@5     | 0.9948     |
+| cosine_recall@10    | 0.9948     |
+| cosine_ndcg@10      | 0.9381     |
+| cosine_mrr@10       | 0.9184     |
+| **cosine_map@100**  | **0.9186** |
+
+<!--
+## 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 |
+|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.0794  | 5       | 5.4149        | -                      | -                      | -                      | -                     | -                      |
+| 0.1587  | 10      | 4.8587        | -                      | -                      | -                      | -                     | -                      |
+| 0.2381  | 15      | 3.9711        | -                      | -                      | -                      | -                     | -                      |
+| 0.3175  | 20      | 3.4853        | -                      | -                      | -                      | -                     | -                      |
+| 0.3968  | 25      | 3.6227        | -                      | -                      | -                      | -                     | -                      |
+| 0.4762  | 30      | 3.3359        | -                      | -                      | -                      | -                     | -                      |
+| 0.5556  | 35      | 2.0868        | -                      | -                      | -                      | -                     | -                      |
+| 0.6349  | 40      | 2.256         | -                      | -                      | -                      | -                     | -                      |
+| 0.7143  | 45      | 2.2958        | -                      | -                      | -                      | -                     | -                      |
+| 0.7937  | 50      | 1.7128        | -                      | -                      | -                      | -                     | -                      |
+| 0.8730  | 55      | 2.029         | -                      | -                      | -                      | -                     | -                      |
+| 0.9524  | 60      | 1.9104        | -                      | -                      | -                      | -                     | -                      |
+| 1.0     | 63      | -             | 0.8950                 | 0.9042                 | 0.9039                 | 0.8640                | 0.8989                 |
+| 1.0317  | 65      | 2.5929        | -                      | -                      | -                      | -                     | -                      |
+| 1.1111  | 70      | 1.4257        | -                      | -                      | -                      | -                     | -                      |
+| 1.1905  | 75      | 1.9956        | -                      | -                      | -                      | -                     | -                      |
+| 1.2698  | 80      | 1.5845        | -                      | -                      | -                      | -                     | -                      |
+| 1.3492  | 85      | 1.7383        | -                      | -                      | -                      | -                     | -                      |
+| 1.4286  | 90      | 1.4657        | -                      | -                      | -                      | -                     | -                      |
+| 1.5079  | 95      | 1.8461        | -                      | -                      | -                      | -                     | -                      |
+| 1.5873  | 100     | 1.8531        | -                      | -                      | -                      | -                     | -                      |
+| 1.6667  | 105     | 1.6504        | -                      | -                      | -                      | -                     | -                      |
+| 1.7460  | 110     | 2.7636        | -                      | -                      | -                      | -                     | -                      |
+| 1.8254  | 115     | 0.7195        | -                      | -                      | -                      | -                     | -                      |
+| 1.9048  | 120     | 1.2494        | -                      | -                      | -                      | -                     | -                      |
+| 1.9841  | 125     | 1.7331        | -                      | -                      | -                      | -                     | -                      |
+| 2.0     | 126     | -             | 0.9170                 | 0.9340                 | 0.9167                 | 0.9013                | 0.9179                 |
+| 2.0635  | 130     | 1.1102        | -                      | -                      | -                      | -                     | -                      |
+| 2.1429  | 135     | 1.8586        | -                      | -                      | -                      | -                     | -                      |
+| 2.2222  | 140     | 1.4211        | -                      | -                      | -                      | -                     | -                      |
+| 2.3016  | 145     | 1.9531        | -                      | -                      | -                      | -                     | -                      |
+| 2.3810  | 150     | 1.9516        | -                      | -                      | -                      | -                     | -                      |
+| 2.4603  | 155     | 2.1174        | -                      | -                      | -                      | -                     | -                      |
+| 2.5397  | 160     | 1.7883        | -                      | -                      | -                      | -                     | -                      |
+| 2.6190  | 165     | 1.4537        | -                      | -                      | -                      | -                     | -                      |
+| 2.6984  | 170     | 1.3927        | -                      | -                      | -                      | -                     | -                      |
+| 2.7778  | 175     | 1.2559        | -                      | -                      | -                      | -                     | -                      |
+| 2.8571  | 180     | 1.8748        | -                      | -                      | -                      | -                     | -                      |
+| 2.9365  | 185     | 0.7509        | -                      | -                      | -                      | -                     | -                      |
+| 3.0     | 189     | -             | 0.9312                 | 0.9244                 | 0.9241                 | 0.9199                | 0.9349                 |
+| 3.0159  | 190     | 0.947         | -                      | -                      | -                      | -                     | -                      |
+| 3.0952  | 195     | 1.9463        | -                      | -                      | -                      | -                     | -                      |
+| 3.1746  | 200     | 1.2077        | -                      | -                      | -                      | -                     | -                      |
+| 3.2540  | 205     | 0.7721        | -                      | -                      | -                      | -                     | -                      |
+| 3.3333  | 210     | 1.5633        | -                      | -                      | -                      | -                     | -                      |
+| 3.4127  | 215     | 1.5042        | -                      | -                      | -                      | -                     | -                      |
+| 3.4921  | 220     | 1.1531        | -                      | -                      | -                      | -                     | -                      |
+| 3.5714  | 225     | 1.2408        | -                      | -                      | -                      | -                     | -                      |
+| 3.6508  | 230     | 0.8085        | -                      | -                      | -                      | -                     | -                      |
+| 3.7302  | 235     | 1.1195        | -                      | -                      | -                      | -                     | -                      |
+| 3.8095  | 240     | 1.1843        | -                      | -                      | -                      | -                     | -                      |
+| 3.8889  | 245     | 0.7176        | -                      | -                      | -                      | -                     | -                      |
+| 3.9683  | 250     | 1.1715        | -                      | -                      | -                      | -                     | -                      |
+| 4.0     | 252     | -             | 0.9244                 | 0.9287                 | 0.9251                 | 0.9199                | 0.9300                 |
+| 4.0476  | 255     | 1.3187        | -                      | -                      | -                      | -                     | -                      |
+| 4.1270  | 260     | 0.2891        | -                      | -                      | -                      | -                     | -                      |
+| 4.2063  | 265     | 1.5887        | -                      | -                      | -                      | -                     | -                      |
+| 4.2857  | 270     | 1.1227        | -                      | -                      | -                      | -                     | -                      |
+| 4.3651  | 275     | 1.5385        | -                      | -                      | -                      | -                     | -                      |
+| 4.4444  | 280     | 0.4732        | -                      | -                      | -                      | -                     | -                      |
+| 4.5238  | 285     | 1.2039        | -                      | -                      | -                      | -                     | -                      |
+| 4.6032  | 290     | 1.0755        | -                      | -                      | -                      | -                     | -                      |
+| 4.6825  | 295     | 1.5345        | -                      | -                      | -                      | -                     | -                      |
+| 4.7619  | 300     | 1.4255        | -                      | -                      | -                      | -                     | -                      |
+| 4.8413  | 305     | 1.7436        | -                      | -                      | -                      | -                     | -                      |
+| 4.9206  | 310     | 0.9408        | -                      | -                      | -                      | -                     | -                      |
+| **5.0** | **315** | **0.7724**    | **0.9269**             | **0.9277**             | **0.9277**             | **0.9186**            | **0.9302**             |
+
+* 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-matryoshka-500",
+  "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 sha256:6b9f7fc7e4b030276e7f59dbcd4b900ace97b92902dd41d969b5dc88b9b2e306
+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]"
+}