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 +1,979 @@
+---
+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:1000
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- 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 is the impact of claim extraction on the efficiency of query generation within
+    various tool querying methodologies?
+  - What are the implications of integrating both attribution and preservation metrics
+    in the assessment of a revised text for an attribution report?
+  - What impact does the calibration of large language models, as discussed in the
+    research by Kadavath et al. (2022), have on the consistency and accuracy of their
+    responses, particularly in the context of multiple choice questions?
+- 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 are the implications of a language model's performance when it is primarily
+    trained on familiar examples compared to a diverse set of unfamiliar examples,
+    and how does this relate to the phenomenon of hallucinations in language models?
+  - How can the insights gained from the evaluation framework inform the future enhancements
+    of AI models, particularly in terms of improving factual accuracy and entity recognition?
+  - What role does the MPNet model play in evaluating the faithfulness of reasoning
+    paths, particularly in relation to scores of entailment and contradiction?
+- 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 methods does the model employ to generate impactful, non-standard verification
+    questions that enhance the fact-checking process?
+  - What impact does the timing of fact presentation in AI outputs have on the likelihood
+    of generating inaccuracies?
+  - What are the benefits of using the 'Factor+revise' strategy in enhancing the reliability
+    of verification processes in few-shot learning, particularly when it comes to
+    identifying inconsistencies?
+- source_sentence: 'Research stage: Find related documents as evidence.
+
+
+    (1) First use a query generation model (via few-shot prompting, $x \to {q_1, \dots,
+    q_N}$) to construct a set of search queries ${q_1, \dots, q_N}$ to verify all
+    aspects of each sentence.
+
+    (2) Run Google search, $K=5$ results per query $q_i$.
+
+    (3) Utilize a pretrained query-document relevance model to assign relevance scores
+    and only retain one most relevant $J=1$ document $e_{i1}, \dots, e_{iJ}$ per query
+    $q_i$.
+
+
+
+    Revision stage: Edit the output to correct content unsupported by evidence while
+    preserving the original content as much as possible. Initialize the revised text
+    $y=x$.'
+  sentences:
+  - In what ways does the process of generating queries facilitate the verification
+    of content accuracy, particularly through the lens of evidence-based editing methodologies?
+  - What role do attribution and preservation metrics play in assessing the quality
+    of revised texts, and how might these factors influence the success of the Evidence
+    Disagreement Detection process?
+  - What are the practical ways to utilize the F1 @ K metric for assessing how well
+    FacTool identifies factual inaccuracies in various fields?
+- source_sentence: '(1) Joint: join with step 2, where the few-shot examples are structured
+    as (response, verification questions, verification answers); The drawback is that
+    the original response is in the context, so the model may repeat similar hallucination.
+
+    (2) 2-step: separate the verification planning and execution steps, such as the
+    original response doesn’t impact
+
+    (3) Factored: each verification question is answered separately. Say, if a long-form
+    base generation results in multiple verification questions, we would answer each
+    question one-by-one.
+
+    (4) Factor+revise: adding a “cross-checking” step after factored verification
+    execution, conditioned on both the baseline response and the verification question
+    and answer. It detects inconsistency.
+
+
+
+    Final output: Generate the final, refined output. The output gets revised at this
+    step if any inconsistency is discovered.'
+  sentences:
+  - What are the key challenges associated with using a pre-training dataset for world
+    knowledge, particularly in maintaining the factual accuracy of the outputs generated
+    by the model?
+  - What obstacles arise when depending on the pre-training dataset in the context
+    of extrinsic hallucination affecting model outputs?
+  - In what ways does the 'Factor+revise' method enhance the reliability of responses
+    when compared to the 'Joint' and '2-step' methods used for cross-checking?
+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: 0.984375
+      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: 0.8802083333333334
+      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: 0.8802083333333334
+      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: 0.9495062223081544
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9337673611111109
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.934240845959596
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8854166666666666
+      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: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8854166666666666
+      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: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8854166666666666
+      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: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9536782535355709
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.937818287037037
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.937818287037037
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.9010416666666666
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.984375
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.9010416666666666
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.328125
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19999999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.9010416666666666
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.984375
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9587563670488631
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9446180555555554
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9446180555555556
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.90625
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.984375
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.90625
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.328125
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19999999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.90625
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.984375
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9609068566179642
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9474826388888888
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.947482638888889
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.890625
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.984375
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.890625
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.328125
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19999999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999999
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.890625
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.984375
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9551401340175182
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9396701388888888
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.939670138888889
+      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-1000")
+# Run inference
+sentences = [
+    '(1) Joint: join with step 2, where the few-shot examples are structured as (response, verification questions, verification answers); The drawback is that the original response is in the context, so the model may repeat similar hallucination.\n(2) 2-step: separate the verification planning and execution steps, such as the original response doesn’t impact\n(3) Factored: each verification question is answered separately. Say, if a long-form base generation results in multiple verification questions, we would answer each question one-by-one.\n(4) Factor+revise: adding a “cross-checking” step after factored verification execution, conditioned on both the baseline response and the verification question and answer. It detects inconsistency.\n\n\nFinal output: Generate the final, refined output. The output gets revised at this step if any inconsistency is discovered.',
+    "In what ways does the 'Factor+revise' method enhance the reliability of responses when compared to the 'Joint' and '2-step' methods used for cross-checking?",
+    'What obstacles arise when depending on the pre-training dataset in the context of extrinsic hallucination affecting model outputs?',
+]
+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   | 0.9844     |
+| cosine_accuracy@5   | 0.9948     |
+| cosine_accuracy@10  | 0.9948     |
+| cosine_precision@1  | 0.8802     |
+| cosine_precision@3  | 0.3281     |
+| cosine_precision@5  | 0.199      |
+| cosine_precision@10 | 0.0995     |
+| cosine_recall@1     | 0.8802     |
+| cosine_recall@3     | 0.9844     |
+| cosine_recall@5     | 0.9948     |
+| cosine_recall@10    | 0.9948     |
+| cosine_ndcg@10      | 0.9495     |
+| cosine_mrr@10       | 0.9338     |
+| **cosine_map@100**  | **0.9342** |
+
+#### 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   | 0.8854     |
+| cosine_accuracy@3   | 0.9844     |
+| cosine_accuracy@5   | 0.9948     |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8854     |
+| cosine_precision@3  | 0.3281     |
+| cosine_precision@5  | 0.199      |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8854     |
+| cosine_recall@3     | 0.9844     |
+| cosine_recall@5     | 0.9948     |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9537     |
+| cosine_mrr@10       | 0.9378     |
+| **cosine_map@100**  | **0.9378** |
+
+#### 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   | 0.901      |
+| cosine_accuracy@3   | 0.9844     |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.901      |
+| cosine_precision@3  | 0.3281     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.901      |
+| cosine_recall@3     | 0.9844     |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9588     |
+| cosine_mrr@10       | 0.9446     |
+| **cosine_map@100**  | **0.9446** |
+
+#### 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   | 0.9062     |
+| cosine_accuracy@3   | 0.9844     |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.9062     |
+| cosine_precision@3  | 0.3281     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.9062     |
+| cosine_recall@3     | 0.9844     |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9609     |
+| cosine_mrr@10       | 0.9475     |
+| **cosine_map@100**  | **0.9475** |
+
+#### 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   | 0.8906     |
+| cosine_accuracy@3   | 0.9844     |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8906     |
+| cosine_precision@3  | 0.3281     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8906     |
+| cosine_recall@3     | 0.9844     |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9551     |
+| cosine_mrr@10       | 0.9397     |
+| **cosine_map@100**  | **0.9397** |
+
+<!--
+## 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
+<details><summary>Click to expand</summary>
+
+| 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 |
+|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.04    | 5       | 4.9678        | -                      | -                      | -                      | -                     | -                      |
+| 0.08    | 10      | 4.6482        | -                      | -                      | -                      | -                     | -                      |
+| 0.12    | 15      | 5.0735        | -                      | -                      | -                      | -                     | -                      |
+| 0.16    | 20      | 4.0336        | -                      | -                      | -                      | -                     | -                      |
+| 0.2     | 25      | 3.7572        | -                      | -                      | -                      | -                     | -                      |
+| 0.24    | 30      | 4.3054        | -                      | -                      | -                      | -                     | -                      |
+| 0.28    | 35      | 2.6705        | -                      | -                      | -                      | -                     | -                      |
+| 0.32    | 40      | 3.1929        | -                      | -                      | -                      | -                     | -                      |
+| 0.36    | 45      | 3.1139        | -                      | -                      | -                      | -                     | -                      |
+| 0.4     | 50      | 2.5219        | -                      | -                      | -                      | -                     | -                      |
+| 0.44    | 55      | 3.1847        | -                      | -                      | -                      | -                     | -                      |
+| 0.48    | 60      | 2.2306        | -                      | -                      | -                      | -                     | -                      |
+| 0.52    | 65      | 2.251         | -                      | -                      | -                      | -                     | -                      |
+| 0.56    | 70      | 2.2432        | -                      | -                      | -                      | -                     | -                      |
+| 0.6     | 75      | 2.7462        | -                      | -                      | -                      | -                     | -                      |
+| 0.64    | 80      | 2.9992        | -                      | -                      | -                      | -                     | -                      |
+| 0.68    | 85      | 2.338         | -                      | -                      | -                      | -                     | -                      |
+| 0.72    | 90      | 2.0169        | -                      | -                      | -                      | -                     | -                      |
+| 0.76    | 95      | 1.257         | -                      | -                      | -                      | -                     | -                      |
+| 0.8     | 100     | 1.5015        | -                      | -                      | -                      | -                     | -                      |
+| 0.84    | 105     | 1.9198        | -                      | -                      | -                      | -                     | -                      |
+| 0.88    | 110     | 2.2154        | -                      | -                      | -                      | -                     | -                      |
+| 0.92    | 115     | 2.4026        | -                      | -                      | -                      | -                     | -                      |
+| 0.96    | 120     | 1.911         | -                      | -                      | -                      | -                     | -                      |
+| 1.0     | 125     | 2.079         | 0.9151                 | 0.9098                 | 0.9220                 | 0.8788                | 0.9251                 |
+| 1.04    | 130     | 1.4704        | -                      | -                      | -                      | -                     | -                      |
+| 1.08    | 135     | 0.7323        | -                      | -                      | -                      | -                     | -                      |
+| 1.12    | 140     | 0.6308        | -                      | -                      | -                      | -                     | -                      |
+| 1.16    | 145     | 0.4655        | -                      | -                      | -                      | -                     | -                      |
+| 1.2     | 150     | 1.0186        | -                      | -                      | -                      | -                     | -                      |
+| 1.24    | 155     | 1.1408        | -                      | -                      | -                      | -                     | -                      |
+| 1.28    | 160     | 1.965         | -                      | -                      | -                      | -                     | -                      |
+| 1.32    | 165     | 1.5987        | -                      | -                      | -                      | -                     | -                      |
+| 1.3600  | 170     | 3.288         | -                      | -                      | -                      | -                     | -                      |
+| 1.4     | 175     | 1.632         | -                      | -                      | -                      | -                     | -                      |
+| 1.44    | 180     | 1.0376        | -                      | -                      | -                      | -                     | -                      |
+| 1.48    | 185     | 0.9466        | -                      | -                      | -                      | -                     | -                      |
+| 1.52    | 190     | 1.0106        | -                      | -                      | -                      | -                     | -                      |
+| 1.56    | 195     | 1.4875        | -                      | -                      | -                      | -                     | -                      |
+| 1.6     | 200     | 1.314         | -                      | -                      | -                      | -                     | -                      |
+| 1.6400  | 205     | 1.3022        | -                      | -                      | -                      | -                     | -                      |
+| 1.6800  | 210     | 1.5312        | -                      | -                      | -                      | -                     | -                      |
+| 1.72    | 215     | 1.7982        | -                      | -                      | -                      | -                     | -                      |
+| 1.76    | 220     | 1.7962        | -                      | -                      | -                      | -                     | -                      |
+| 1.8     | 225     | 1.5788        | -                      | -                      | -                      | -                     | -                      |
+| 1.8400  | 230     | 1.152         | -                      | -                      | -                      | -                     | -                      |
+| 1.88    | 235     | 2.0556        | -                      | -                      | -                      | -                     | -                      |
+| 1.92    | 240     | 1.3165        | -                      | -                      | -                      | -                     | -                      |
+| 1.96    | 245     | 0.6941        | -                      | -                      | -                      | -                     | -                      |
+| **2.0** | **250** | **1.2239**    | **0.9404**             | **0.944**              | **0.9427**             | **0.9327**            | **0.9424**             |
+| 2.04    | 255     | 1.0423        | -                      | -                      | -                      | -                     | -                      |
+| 2.08    | 260     | 0.8893        | -                      | -                      | -                      | -                     | -                      |
+| 2.12    | 265     | 1.2859        | -                      | -                      | -                      | -                     | -                      |
+| 2.16    | 270     | 1.4505        | -                      | -                      | -                      | -                     | -                      |
+| 2.2     | 275     | 0.2728        | -                      | -                      | -                      | -                     | -                      |
+| 2.24    | 280     | 0.6588        | -                      | -                      | -                      | -                     | -                      |
+| 2.2800  | 285     | 0.8014        | -                      | -                      | -                      | -                     | -                      |
+| 2.32    | 290     | 0.3053        | -                      | -                      | -                      | -                     | -                      |
+| 2.36    | 295     | 1.4289        | -                      | -                      | -                      | -                     | -                      |
+| 2.4     | 300     | 1.1458        | -                      | -                      | -                      | -                     | -                      |
+| 2.44    | 305     | 0.6987        | -                      | -                      | -                      | -                     | -                      |
+| 2.48    | 310     | 1.3389        | -                      | -                      | -                      | -                     | -                      |
+| 2.52    | 315     | 1.2991        | -                      | -                      | -                      | -                     | -                      |
+| 2.56    | 320     | 1.8088        | -                      | -                      | -                      | -                     | -                      |
+| 2.6     | 325     | 0.4242        | -                      | -                      | -                      | -                     | -                      |
+| 2.64    | 330     | 1.5873        | -                      | -                      | -                      | -                     | -                      |
+| 2.68    | 335     | 1.3873        | -                      | -                      | -                      | -                     | -                      |
+| 2.7200  | 340     | 1.4297        | -                      | -                      | -                      | -                     | -                      |
+| 2.76    | 345     | 2.0637        | -                      | -                      | -                      | -                     | -                      |
+| 2.8     | 350     | 1.1252        | -                      | -                      | -                      | -                     | -                      |
+| 2.84    | 355     | 0.367         | -                      | -                      | -                      | -                     | -                      |
+| 2.88    | 360     | 1.7606        | -                      | -                      | -                      | -                     | -                      |
+| 2.92    | 365     | 1.196         | -                      | -                      | -                      | -                     | -                      |
+| 2.96    | 370     | 1.8827        | -                      | -                      | -                      | -                     | -                      |
+| 3.0     | 375     | 0.6822        | 0.9494                 | 0.9479                 | 0.9336                 | 0.9414                | 0.9405                 |
+| 3.04    | 380     | 0.4954        | -                      | -                      | -                      | -                     | -                      |
+| 3.08    | 385     | 0.1717        | -                      | -                      | -                      | -                     | -                      |
+| 3.12    | 390     | 0.7435        | -                      | -                      | -                      | -                     | -                      |
+| 3.16    | 395     | 1.4323        | -                      | -                      | -                      | -                     | -                      |
+| 3.2     | 400     | 1.1207        | -                      | -                      | -                      | -                     | -                      |
+| 3.24    | 405     | 1.9009        | -                      | -                      | -                      | -                     | -                      |
+| 3.2800  | 410     | 1.6706        | -                      | -                      | -                      | -                     | -                      |
+| 3.32    | 415     | 0.8378        | -                      | -                      | -                      | -                     | -                      |
+| 3.36    | 420     | 1.0911        | -                      | -                      | -                      | -                     | -                      |
+| 3.4     | 425     | 0.6565        | -                      | -                      | -                      | -                     | -                      |
+| 3.44    | 430     | 1.0302        | -                      | -                      | -                      | -                     | -                      |
+| 3.48    | 435     | 0.6425        | -                      | -                      | -                      | -                     | -                      |
+| 3.52    | 440     | 1.1472        | -                      | -                      | -                      | -                     | -                      |
+| 3.56    | 445     | 1.996         | -                      | -                      | -                      | -                     | -                      |
+| 3.6     | 450     | 1.5308        | -                      | -                      | -                      | -                     | -                      |
+| 3.64    | 455     | 0.7427        | -                      | -                      | -                      | -                     | -                      |
+| 3.68    | 460     | 1.4596        | -                      | -                      | -                      | -                     | -                      |
+| 3.7200  | 465     | 1.1984        | -                      | -                      | -                      | -                     | -                      |
+| 3.76    | 470     | 0.7601        | -                      | -                      | -                      | -                     | -                      |
+| 3.8     | 475     | 1.3544        | -                      | -                      | -                      | -                     | -                      |
+| 3.84    | 480     | 1.6655        | -                      | -                      | -                      | -                     | -                      |
+| 3.88    | 485     | 1.2596        | -                      | -                      | -                      | -                     | -                      |
+| 3.92    | 490     | 0.9451        | -                      | -                      | -                      | -                     | -                      |
+| 3.96    | 495     | 0.7079        | -                      | -                      | -                      | -                     | -                      |
+| 4.0     | 500     | 1.3471        | 0.9453                 | 0.9446                 | 0.9404                 | 0.9371                | 0.9335                 |
+| 4.04    | 505     | 0.4583        | -                      | -                      | -                      | -                     | -                      |
+| 4.08    | 510     | 1.288         | -                      | -                      | -                      | -                     | -                      |
+| 4.12    | 515     | 1.6946        | -                      | -                      | -                      | -                     | -                      |
+| 4.16    | 520     | 1.1239        | -                      | -                      | -                      | -                     | -                      |
+| 4.2     | 525     | 1.1026        | -                      | -                      | -                      | -                     | -                      |
+| 4.24    | 530     | 1.4121        | -                      | -                      | -                      | -                     | -                      |
+| 4.28    | 535     | 1.7113        | -                      | -                      | -                      | -                     | -                      |
+| 4.32    | 540     | 0.8389        | -                      | -                      | -                      | -                     | -                      |
+| 4.36    | 545     | 0.3117        | -                      | -                      | -                      | -                     | -                      |
+| 4.4     | 550     | 0.3144        | -                      | -                      | -                      | -                     | -                      |
+| 4.44    | 555     | 1.4694        | -                      | -                      | -                      | -                     | -                      |
+| 4.48    | 560     | 1.3233        | -                      | -                      | -                      | -                     | -                      |
+| 4.52    | 565     | 0.792         | -                      | -                      | -                      | -                     | -                      |
+| 4.5600  | 570     | 0.4881        | -                      | -                      | -                      | -                     | -                      |
+| 4.6     | 575     | 0.5097        | -                      | -                      | -                      | -                     | -                      |
+| 4.64    | 580     | 1.6377        | -                      | -                      | -                      | -                     | -                      |
+| 4.68    | 585     | 0.7273        | -                      | -                      | -                      | -                     | -                      |
+| 4.72    | 590     | 1.5464        | -                      | -                      | -                      | -                     | -                      |
+| 4.76    | 595     | 1.4392        | -                      | -                      | -                      | -                     | -                      |
+| 4.8     | 600     | 1.4384        | -                      | -                      | -                      | -                     | -                      |
+| 4.84    | 605     | 0.6375        | -                      | -                      | -                      | -                     | -                      |
+| 4.88    | 610     | 1.0528        | -                      | -                      | -                      | -                     | -                      |
+| 4.92    | 615     | 0.0276        | -                      | -                      | -                      | -                     | -                      |
+| 4.96    | 620     | 0.9604        | -                      | -                      | -                      | -                     | -                      |
+| 5.0     | 625     | 0.7219        | 0.9475                 | 0.9446                 | 0.9378                 | 0.9397                | 0.9342                 |
+
+* The bold row denotes the saved checkpoint.
+</details>
+
+### 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-1000",
+  "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 sha256:6cdc269cebe22fdfbe50ecfa92eb64f63b070c37c8d297df898784ba0877fa51
+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]"
+}