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1_Pooling/config.json

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+{
+  "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

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+---
+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: What considerations should be taken into account regarding the
+    specific set or types of users for the AI system?
+  sentences:
+  - "46 \nMG-4.3-003 \nReport GAI incidents in compliance with legal and regulatory\
+    \ requirements (e.g., \nHIPAA breach reporting, e.g., OCR (2023) or NHTSA (2022)\
+    \ autonomous vehicle \ncrash reporting requirements. \nInformation Security; Data\
+    \ Privacy \nAI Actor Tasks: AI Deployment, Affected Individuals and Communities,\
+    \ Domain Experts, End-Users, Human Factors, Operation and \nMonitoring"
+  - "reporting, data protection, data privacy, or other laws. \nData Privacy; Human-AI\
+    \ \nConfiguration; Information \nSecurity; Value Chain and \nComponent Integration;\
+    \ Harmful \nBias and Homogenization \nGV-6.2-004 \nEstablish policies and procedures\
+    \ for continuous monitoring of third-party GAI \nsystems in deployment. \nValue\
+    \ Chain and Component \nIntegration \nGV-6.2-005 \nEstablish policies and procedures\
+    \ that address GAI data redundancy, including \nmodel weights and other system\
+    \ artifacts."
+  - "times, and availability of critical support. \nHuman-AI Configuration; \nInformation\
+    \ Security; Value Chain \nand Component Integration \nAI Actor Tasks: AI Deployment,\
+    \ Operation and Monitoring, TEVV, Third-party entities \n \nMAP 1.1: Intended\
+    \ purposes, potentially beneficial uses, context specific laws, norms and expectations,\
+    \ and prospective settings in \nwhich the AI system will be deployed are understood\
+    \ and documented. Considerations include: the specific set or types of users"
+- source_sentence: What should organizations leverage when deploying GAI applications
+    and using third-party pre-trained models?
+  sentences:
+  - "external use, narrow vs. broad application scope, fine-tuning, and varieties of\
+    \ \ndata sources (e.g., grounding, retrieval-augmented generation). \nData Privacy;\
+    \ Intellectual \nProperty"
+  - "44 \nMG-3.2-007 \nLeverage feedback and recommendations from organizational boards\
+    \ or \ncommittees related to the deployment of GAI applications and content \n\
+    provenance when using third-party pre-trained models. \nInformation Integrity;\
+    \ Value Chain \nand Component Integration \nMG-3.2-008 \nUse human moderation\
+    \ systems where appropriate to review generated content \nin accordance with human-AI\
+    \ configuration policies established in the Govern"
+  - "Security \nMS-2.7-003 \nConduct user surveys to gather user satisfaction with\
+    \ the AI-generated content \nand user perceptions of content authenticity. Analyze\
+    \ user feedback to identify \nconcerns and/or current literacy levels related\
+    \ to content provenance and \nunderstanding of labels on content. \nHuman-AI Configuration;\
+    \ \nInformation Integrity \nMS-2.7-004 \nIdentify metrics that reflect the effectiveness\
+    \ of security measures, such as data"
+- source_sentence: What are the potential positive and negative impacts of AI system
+    uses on individuals and communities?
+  sentences:
+  - "and Homogenization \nAI Actor Tasks: AI Deployment, Affected Individuals and Communities,\
+    \ End-Users, Operation and Monitoring, TEVV \n \nMEASURE 4.2: Measurement results\
+    \ regarding AI system trustworthiness in deployment context(s) and across the\
+    \ AI lifecycle are \ninformed by input from domain experts and relevant AI Actors\
+    \ to validate whether the system is performing consistently as \nintended. Results\
+    \ are documented. \nAction ID \nSuggested Action \nGAI Risks \nMS-4.2-001"
+  - "bias based on race, gender, disability, or other protected classes.  \nHarmful\
+    \ bias in GAI systems can also lead to harms via disparities between how a model\
+    \ performs for \ndifferent subgroups or languages (e.g., an LLM may perform less\
+    \ well for non-English languages or \ncertain dialects). Such disparities can\
+    \ contribute to discriminatory decision-making or amplification of \nexisting societal\
+    \ biases. In addition, GAI systems may be inappropriately trusted to perform similarly"
+  - "along with their expectations; potential positive and negative impacts of system\
+    \ uses to individuals, communities, organizations, \nsociety, and the planet;\
+    \ assumptions and related limitations about AI system purposes, uses, and risks\
+    \ across the development or \nproduct AI lifecycle; and related TEVV and system\
+    \ metrics. \nAction ID \nSuggested Action \nGAI Risks \nMP-1.1-001 \nWhen identifying\
+    \ intended purposes, consider factors such as internal vs."
+- source_sentence: How does the suggested action MG-41-001 aim to address GAI risks?
+  sentences:
+  - "most appropriate baseline is to compare against, which can result in divergent\
+    \ views on when a disparity between \nAI behaviors for different subgroups constitutes\
+    \ a harm. In discussing harms from disparities such as biased \nbehavior, this\
+    \ document highlights examples where someone’s situation is worsened relative\
+    \ to what it would have \nbeen in the absence of any AI system, making the outcome\
+    \ unambiguously a harm of the system."
+  - "Harmful Bias Managed, Privacy Enhanced, Safe, Secure and Resilient, Valid and\
+    \ Reliable \n3. \nSuggested Actions to Manage GAI Risks \nThe following suggested\
+    \ actions target risks unique to or exacerbated by GAI. \nIn addition to the suggested\
+    \ actions below, AI risk management activities and actions set forth in the AI\
+    \ \nRMF 1.0 and Playbook are already applicable for managing GAI risks. Organizations\
+    \ are encouraged to"
+  - "MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including\
+    \ mechanisms for capturing and evaluating \ninput from users and other relevant\
+    \ AI Actors, appeal and override, decommissioning, incident response, recovery,\
+    \ and change \nmanagement. \nAction ID \nSuggested Action \nGAI Risks \nMG-4.1-001\
+    \ \nCollaborate with external researchers, industry experts, and community \n\
+    representatives to maintain awareness of emerging best practices and"
+- source_sentence: What are some examples of input data features that may serve as
+    proxies for demographic group membership in GAI systems?
+  sentences:
+  - "data privacy violations, obscenity, extremism, violence, or CBRN information\
+    \ in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene,\
+    \ Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous,\
+    \ \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003\
+    \ Re-evaluate safety features of fine-tuned models when the negative risk exceeds\
+    \ \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent"
+  - "GAI. \nInformation Integrity; Intellectual \nProperty \nAI Actor Tasks: Governance\
+    \ and Oversight, Operation and Monitoring \n \nGOVERN 1.6: Mechanisms are in place\
+    \ to inventory AI systems and are resourced according to organizational risk priorities.\
+    \ \nAction ID \nSuggested Action \nGAI Risks \nGV-1.6-001 Enumerate organizational\
+    \ GAI systems for incorporation into AI system inventory \nand adjust AI system\
+    \ inventory requirements to account for GAI risks. \nInformation Security"
+  - "complex or unstructured data; Input data features that may serve as proxies for\
+    \ \ndemographic group membership (i.e., image metadata, language dialect) or \n\
+    otherwise give rise to emergent bias within GAI systems; The extent to which \n\
+    the digital divide may negatively impact representativeness in GAI system \ntraining\
+    \ and TEVV data; Filtering of hate speech or content in GAI system \ntraining\
+    \ data; Prevalence of GAI-generated data in GAI system training data. \nHarmful\
+    \ Bias and Homogenization"
+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: 0.975
+      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: 0.85
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.325
+      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: 0.85
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.975
+      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: 0.9341754705038519
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.911875
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9118749999999999
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.85
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.975
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 1.0
+      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: 0.325
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.19999999999999998
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.09999999999999999
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.85
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.975
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 1.0
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 1.0
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9341754705038519
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.911875
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.9118749999999999
+      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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What are some examples of input data features that may serve as proxies for demographic group membership in GAI systems?',
+    'complex or unstructured data; Input data features that may serve as proxies for \ndemographic group membership (i.e., image metadata, language dialect) or \notherwise give rise to emergent bias within GAI systems; The extent to which \nthe digital divide may negatively impact representativeness in GAI system \ntraining and TEVV data; Filtering of hate speech or content in GAI system \ntraining data; Prevalence of GAI-generated data in GAI system training data. \nHarmful Bias and Homogenization',
+    'GAI. \nInformation Integrity; Intellectual \nProperty \nAI Actor Tasks: Governance and Oversight, Operation and Monitoring \n \nGOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities. \nAction ID \nSuggested Action \nGAI Risks \nGV-1.6-001 Enumerate organizational GAI systems for incorporation into AI system inventory \nand adjust AI system inventory requirements to account for GAI risks. \nInformation Security',
+]
+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   | 0.975      |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.85       |
+| cosine_precision@3  | 0.325      |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.85       |
+| cosine_recall@3     | 0.975      |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9342     |
+| cosine_mrr@10       | 0.9119     |
+| **cosine_map@100**  | **0.9119** |
+| dot_accuracy@1      | 0.85       |
+| dot_accuracy@3      | 0.975      |
+| dot_accuracy@5      | 1.0        |
+| dot_accuracy@10     | 1.0        |
+| dot_precision@1     | 0.85       |
+| dot_precision@3     | 0.325      |
+| dot_precision@5     | 0.2        |
+| dot_precision@10    | 0.1        |
+| dot_recall@1        | 0.85       |
+| dot_recall@3        | 0.975      |
+| dot_recall@5        | 1.0        |
+| dot_recall@10       | 1.0        |
+| dot_ndcg@10         | 0.9342     |
+| dot_mrr@10          | 0.9119     |
+| dot_map@100         | 0.9119     |
+
+<!--
+## 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: 20.85 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 89.39 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 artificial intelligence?</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`: 20
+- `per_device_eval_batch_size`: 20
+- `num_train_epochs`: 5
+- `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`: 20
+- `per_device_eval_batch_size`: 20
+- `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`: 5
+- `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    | 30   | 0.9271         |
+| 1.6667 | 50   | 0.9306         |
+| 2.0    | 60   | 0.9187         |
+| 3.0    | 90   | 0.9244         |
+| 3.3333 | 100  | 0.9244         |
+| 4.0    | 120  | 0.9244         |
+| 5.0    | 150  | 0.9119         |
+
+
+### 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": "Snowflake/snowflake-arctic-embed-m",
+  "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 sha256:4fa5f54dac2d81eb7dd8f0a39be92c55bb2322e601c15e674b1565465fab4517
+size 435588776

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
+    },
+    "103": {
+      "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]"
+}