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 +1,641 @@
+---
+base_model: Snowflake/snowflake-arctic-embed-m
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:600
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: How can organizations tailor their measurement of GAI risks based
+    on specific characteristics?
+  sentences:
+  - "3 \nthe abuse, misuse, and unsafe repurposing by humans (adversarial or not),\
+    \ and others result \nfrom interactions between a human and an AI system.  \n\
+    • \nTime scale: GAI risks may materialize abruptly or across extended periods.\
+    \ Examples include \nimmediate (and/or prolonged) emotional harm and potential\
+    \ risks to physical safety due to the \ndistribution of harmful deepfake images,\
+    \ or the long-term effect of disinformation on societal \ntrust in public institutions."
+  - "12 \nCSAM. Even when trained on “clean” data, increasingly capable GAI models\
+    \ can synthesize or produce \nsynthetic NCII and CSAM. Websites, mobile apps,\
+    \ and custom-built models that generate synthetic NCII \nhave moved from niche\
+    \ internet forums to mainstream, automated, and scaled online businesses.  \n\
+    Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe, Privacy\
+    \ Enhanced \n2.12. \nValue Chain and Component Integration"
+  - "case context. \nOrganizations may choose to tailor how they measure GAI risks\
+    \ based on these characteristics. They may \nadditionally wish to allocate risk\
+    \ management resources relative to the severity and likelihood of \nnegative impacts,\
+    \ including where and how these risks manifest, and their direct and material\
+    \ impacts \nharms in the context of GAI use. Mitigations for model or system level\
+    \ risks may differ from mitigations \nfor use-case or ecosystem level risks."
+- source_sentence: What methods are suggested for measuring the reliability of content
+    authentication techniques in the context of content provenance?
+  sentences:
+  - "updates. \nInformation Integrity; Data Privacy \nMG-3.2-003 \nDocument sources\
+    \ and types of training data and their origins, potential biases \npresent in\
+    \ the data related to the GAI application and its content provenance, \narchitecture,\
+    \ training process of the pre-trained model including information on \nhyperparameters,\
+    \ training duration, and any fine-tuning or retrieval-augmented \ngeneration processes\
+    \ applied. \nInformation Integrity; Harmful Bias \nand Homogenization; Intellectual\
+    \ \nProperty"
+  - "Security \nMS-2.7-005 \nMeasure reliability of content authentication methods,\
+    \ such as watermarking, \ncryptographic signatures, digital fingerprints, as well\
+    \ as access controls, \nconformity assessment, and model integrity verification,\
+    \ which can help support \nthe effective implementation of content provenance techniques.\
+    \ Evaluate the \nrate of false positives and false negatives in content provenance,\
+    \ as well as true \npositives and true negatives for verification. \nInformation\
+    \ Integrity \nMS-2.7-006"
+  - "GV-1.6-003 \nIn addition to general model, governance, and risk information,\
+    \ consider the \nfollowing items in GAI system inventory entries: Data provenance\
+    \ information \n(e.g., source, signatures, versioning, watermarks); Known issues\
+    \ reported from \ninternal bug tracking or external information sharing resources\
+    \ (e.g., AI incident \ndatabase, AVID, CVE, NVD, or OECD AI incident monitor);\
+    \ Human oversight roles \nand responsibilities; Special rights and considerations\
+    \ for intellectual property,"
+- source_sentence: What are the suggested actions an organization can take to manage
+    GAI risks?
+  sentences:
+  - "Information Integrity; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation\
+    \ or Capabilities \nGV-1.3-007 Devise a plan to halt development or deployment\
+    \ of a GAI system that poses \nunacceptable negative risk. \nCBRN Information\
+    \ and Capability; \nInformation Security; Information \nIntegrity \nAI Actor Tasks:\
+    \ Governance and Oversight \n \nGOVERN 1.4: The risk management process and its\
+    \ outcomes are established through transparent policies, procedures, and other"
+  - "match the statistical properties of real-world data without disclosing personally\
+    \ \nidentifiable information or contributing to homogenization. \nData Privacy;\
+    \ Intellectual Property; \nInformation Integrity; \nConfabulation; Harmful Bias\
+    \ and \nHomogenization \nAI Actor Tasks: AI Deployment, AI Impact Assessment,\
+    \ Governance and Oversight, Operation and Monitoring \n \nMANAGE 2.3: Procedures\
+    \ are followed to respond to and recover from a previously unknown risk when it\
+    \ is identified. \nAction ID"
+  - "• \nSuggested Action: Steps an organization or AI actor can take to manage GAI\
+    \ risks.  \n• \nGAI Risks: Tags linking suggested actions with relevant GAI risks.\
+    \  \n• \nAI Actor Tasks: Pertinent AI Actor Tasks for each subcategory. Not every\
+    \ AI Actor Task listed will \napply to every suggested action in the subcategory\
+    \ (i.e., some apply to AI development and \nothers apply to AI deployment).  \n\
+    The tables below begin with the AI RMF subcategory, shaded in blue, followed by\
+    \ suggested actions."
+- source_sentence: How can harmful bias and homogenization be addressed in the context
+    of human-AI configuration?
+  sentences:
+  - "on GAI, apply general fairness metrics (e.g., demographic parity, equalized odds,\
+    \ \nequal opportunity, statistical hypothesis tests), to the pipeline or business\
+    \ \noutcome where appropriate; Custom, context-specific metrics developed in \n\
+    collaboration with domain experts and affected communities; Measurements of \n\
+    the prevalence of denigration in generated content in deployment (e.g., sub-\n\
+    sampling a fraction of traffic and manually annotating denigrating content). \n\
+    Harmful Bias and Homogenization;"
+  - "MP-5.1-001 Apply TEVV practices for content provenance (e.g., probing a system's\
+    \ synthetic \ndata generation capabilities for potential misuse or vulnerabilities.\
+    \ \nInformation Integrity; Information \nSecurity \nMP-5.1-002 \nIdentify potential\
+    \ content provenance harms of GAI, such as misinformation or \ndisinformation,\
+    \ deepfakes, including NCII, or tampered content. Enumerate and \nrank risks based\
+    \ on their likelihood and potential impact, and determine how well"
+  - "MS-1.3-002 \nEngage in internal and external evaluations, GAI red-teaming, impact\
+    \ \nassessments, or other structured human feedback exercises in consultation\
+    \ \nwith representative AI Actors with expertise and familiarity in the context\
+    \ of \nuse, and/or who are representative of the populations associated with the\
+    \ \ncontext of use. \nHuman-AI Configuration; Harmful \nBias and Homogenization;\
+    \ CBRN \nInformation or Capabilities \nMS-1.3-003"
+- source_sentence: How can structured human feedback exercises, such as GAI red-teaming,
+    contribute to GAI risk measurement and management?
+  sentences:
+  - "rank risks based on their likelihood and potential impact, and determine how\
+    \ well \nprovenance solutions address specific risks and/or harms. \nInformation\
+    \ Integrity; Dangerous, \nViolent, or Hateful Content; \nObscene, Degrading, and/or\
+    \ \nAbusive Content \nMP-5.1-003 \nConsider disclosing use of GAI to end users\
+    \ in relevant contexts, while considering \nthe objective of disclosure, the context\
+    \ of use, the likelihood and magnitude of the"
+  - "15 \nGV-1.3-004 Obtain input from stakeholder communities to identify unacceptable\
+    \ use, in \naccordance with activities in the AI RMF Map function. \nCBRN Information\
+    \ or Capabilities; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias\
+    \ \nand Homogenization; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005\
+    \ \nMaintain an updated hierarchy of identified and expected GAI risks connected\
+    \ to \ncontexts of GAI model advancement and use, potentially including specialized\
+    \ risk"
+  - "AI-generated content, for example by employing techniques like chaos \nengineering\
+    \ and seeking stakeholder feedback. \nInformation Integrity \nMS-1.1-008 \nDefine\
+    \ use cases, contexts of use, capabilities, and negative impacts where \nstructured\
+    \ human feedback exercises, e.g., GAI red-teaming, would be most \nbeneficial for\
+    \ GAI risk measurement and management based on the context of \nuse. \nHarmful\
+    \ Bias and \nHomogenization; CBRN \nInformation or Capabilities \nMS-1.1-009"
+model-index:
+- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.85
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.96
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.98
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.85
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.31999999999999995
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19599999999999995
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.85
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.96
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.98
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9342942871848772
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9124166666666668
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9124166666666668
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.85
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.96
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.98
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 1.0
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.85
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.31999999999999995
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.19599999999999995
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.09999999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.85
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.96
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.98
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 1.0
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9342942871848772
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.9124166666666668
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.9124166666666668
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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': False}) 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("Cheselle/finetuned-arctic-sentence")
+# Run inference
+sentences = [
+    'How can structured human feedback exercises, such as GAI red-teaming, contribute to GAI risk measurement and management?',
+    'AI-generated content, for example by employing techniques like chaos \nengineering and seeking stakeholder feedback. \nInformation Integrity \nMS-1.1-008 \nDefine use cases, contexts of use, capabilities, and negative impacts where \nstructured human feedback exercises, e.g., GAI red-teaming, would be most \nbeneficial for GAI risk measurement and management based on the context of \nuse. \nHarmful Bias and \nHomogenization; CBRN \nInformation or Capabilities \nMS-1.1-009',
+    '15 \nGV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use, in \naccordance with activities in the AI RMF Map function. \nCBRN Information or Capabilities; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias \nand Homogenization; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005 \nMaintain an updated hierarchy of identified and expected GAI risks connected to \ncontexts of GAI model advancement and use, potentially including specialized risk',
+]
+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
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.85       |
+| cosine_accuracy@3   | 0.96       |
+| cosine_accuracy@5   | 0.98       |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.85       |
+| cosine_precision@3  | 0.32       |
+| cosine_precision@5  | 0.196      |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.85       |
+| cosine_recall@3     | 0.96       |
+| cosine_recall@5     | 0.98       |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9343     |
+| cosine_mrr@10       | 0.9124     |
+| **cosine_map@100**  | **0.9124** |
+| dot_accuracy@1      | 0.85       |
+| dot_accuracy@3      | 0.96       |
+| dot_accuracy@5      | 0.98       |
+| dot_accuracy@10     | 1.0        |
+| dot_precision@1     | 0.85       |
+| dot_precision@3     | 0.32       |
+| dot_precision@5     | 0.196      |
+| dot_precision@10    | 0.1        |
+| dot_recall@1        | 0.85       |
+| dot_recall@3        | 0.96       |
+| dot_recall@5        | 0.98       |
+| dot_recall@10       | 1.0        |
+| dot_ndcg@10         | 0.9343     |
+| dot_mrr@10          | 0.9124     |
+| dot_map@100         | 0.9124     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 600 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 600 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 21.05 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 91.74 tokens</li><li>max: 335 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                    | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:--------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the title of the publication related to Artificial Intelligence Risk Management by NIST?</code> | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600-1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br> <br> <br>This publication is available free of charge from: <br>https://doi.org/10.6028/NIST.AI.600-1</code>                                                                                                                                                                                                                                                                |
+  | <code>Where can the NIST AI 600-1 publication be accessed for free?</code>                                    | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600-1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br> <br> <br>This publication is available free of charge from: <br>https://doi.org/10.6028/NIST.AI.600-1</code>                                                                                                                                                                                                                                                                |
+  | <code>What is the title of the publication released by NIST in July 2024 regarding AI risk management?</code> | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600-1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br> <br> <br>This publication is available free of charge from: <br>https://doi.org/10.6028/NIST.AI.600-1 <br> <br>July 2024 <br> <br> <br> <br> <br>U.S. Department of Commerce  <br>Gina M. Raimondo, Secretary <br>National Institute of Standards and Technology  <br>Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `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
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | cosine_map@100 |
+|:------:|:----:|:--------------:|
+| 1.0    | 38   | 0.9033         |
+| 1.3158 | 50   | 0.9067         |
+| 2.0    | 76   | 0.9124         |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.0
+- 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 +1,26 @@
+{
+  "_name_or_path": "finetuned_arctic_sentence",
+  "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,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0526dac2ed2ed7fce8885cdc712f58e87f21390739d6509d7252d04cef25c17
+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": false
+}

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 +1,62 @@
+{
+  "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
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "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]"
+}