finetuned_arctic-embedd-l

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. It maps sentences & paragraphs to a 1024-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-l <!-- at revision 9a9e5834d2e89cdd8bb72b64111dde496e4fe78c -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("jeevanions/finetuned_arctic-embedd-l")
# Run inference
sentences = [
    'How should risks or trustworthiness characteristics that cannot be measured be documented?',
    'MEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for \nimplementation starting with the most significant AI risks. The risks or trustworthiness characteristics that will not – or cannot – be \nmeasured are properly documented. \nAction ID \nSuggested Action \nGAI Risks \nMS-1.1-001 Employ methods to trace the origin and modifications of digital content. \nInformation Integrity \nMS-1.1-002 \nIntegrate tools designed to analyze content provenance and detect data \nanomalies, verify the authenticity of digital signatures, and identify patterns \nassociated with misinformation or manipulation. \nInformation Integrity \nMS-1.1-003 \nDisaggregate evaluation metrics by demographic factors to identify any \ndiscrepancies in how content provenance mechanisms work across diverse \npopulations. \nInformation Integrity; Harmful \nBias and Homogenization \nMS-1.1-004 Develop a suite of metrics to evaluate structured public feedback exercises',
    'existing human performance considered as a performance baseline for the algorithm to meet pre-deployment, \nand as a lifecycle minimum performance standard. Decision possibilities resulting from performance testing \nshould include the possibility of not deploying the system. \nRisk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten\xad\ntial risks of the automated system should be identified and mitigated. Identified risks should focus on the \npotential for meaningful impact on people’s rights, opportunities, or access and include those to impacted \ncommunities that may not be direct users of the automated system, risks resulting from purposeful misuse of \nthe system, and other concerns identified via the consultation process. Assessment and, where possible, mea\xad\nsurement of the impact of risks should be included and balanced such that high impact risks receive attention',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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>

<!-- ## 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: 3,430 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 8 tokens</li><li>mean: 17.71 tokens</li><li>max: 36 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 172.72 tokens</li><li>max: 356 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>What are the key steps to obtain input from stakeholder communities to identify unacceptable use in AI systems?</code>	<code>15 <br>GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use, in <br>accordance with activities in the AI RMF Map function. <br>CBRN Information or Capabilities; <br>Obscene, Degrading, and/or <br>Abusive Content; Harmful Bias <br>and Homogenization; Dangerous, <br>Violent, or Hateful Content <br>GV-1.3-005 <br>Maintain an updated hierarchy of identified and expected GAI risks connected to <br>contexts of GAI model advancement and use, potentially including specialized risk <br>levels for GAI systems that address issues such as model collapse and algorithmic <br>monoculture. <br>Harmful Bias and Homogenization <br>GV-1.3-006 <br>Reevaluate organizational risk tolerances to account for unacceptable negative risk <br>(such as where significant negative impacts are imminent, severe harms are <br>actually occurring, or large-scale risks could occur); and broad GAI negative risks, <br>including: Immature safety or risk cultures related to AI and GAI design, <br>development and deployment, public information integrity risks, including impacts</code>
  <code>How can organizations maintain an updated hierarchy of identified and expected GAI risks?</code>	<code>15 <br>GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use, in <br>accordance with activities in the AI RMF Map function. <br>CBRN Information or Capabilities; <br>Obscene, Degrading, and/or <br>Abusive Content; Harmful Bias <br>and Homogenization; Dangerous, <br>Violent, or Hateful Content <br>GV-1.3-005 <br>Maintain an updated hierarchy of identified and expected GAI risks connected to <br>contexts of GAI model advancement and use, potentially including specialized risk <br>levels for GAI systems that address issues such as model collapse and algorithmic <br>monoculture. <br>Harmful Bias and Homogenization <br>GV-1.3-006 <br>Reevaluate organizational risk tolerances to account for unacceptable negative risk <br>(such as where significant negative impacts are imminent, severe harms are <br>actually occurring, or large-scale risks could occur); and broad GAI negative risks, <br>including: Immature safety or risk cultures related to AI and GAI design, <br>development and deployment, public information integrity risks, including impacts</code>
  <code>What are some examples of unacceptable uses of AI as identified by stakeholder communities?</code>	<code>15 <br>GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use, in <br>accordance with activities in the AI RMF Map function. <br>CBRN Information or Capabilities; <br>Obscene, Degrading, and/or <br>Abusive Content; Harmful Bias <br>and Homogenization; Dangerous, <br>Violent, or Hateful Content <br>GV-1.3-005 <br>Maintain an updated hierarchy of identified and expected GAI risks connected to <br>contexts of GAI model advancement and use, potentially including specialized risk <br>levels for GAI systems that address issues such as model collapse and algorithmic <br>monoculture. <br>Harmful Bias and Homogenization <br>GV-1.3-006 <br>Reevaluate organizational risk tolerances to account for unacceptable negative risk <br>(such as where significant negative impacts are imminent, severe harms are <br>actually occurring, or large-scale risks could occur); and broad GAI negative risks, <br>including: Immature safety or risk cultures related to AI and GAI design, <br>development and deployment, public information integrity risks, including impacts</code>

• Loss: <code>MatryoshkaLoss</code> with these parameters:

  {
      "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: 1
• per_device_eval_batch_size: 1
• 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: 1
• per_device_eval_batch_size: 1
• 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

<details><summary>Click to expand</summary> </details>

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: 2.14.4
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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

@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

@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.* -->