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

This is a sentence-transformers model finetuned from 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
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity

Model Sources

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:

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("XicoC/midterm-finetuned-arctic")
# Run inference
sentences = [
    'What are the key components involved in ensuring data quality and ethical considerations in AI systems?',
    'Data quality; Model architecture (e.g., convolutional neural network, transformers, etc.); Optimizatio n objectives; Training algorithms; RLHF \napproaches; Fine -tuning or retrieval- augmented generation approaches; \nEvaluation data; Ethical considerations; Legal and regulatory requirements.  Information Integrity ; Harmful Bias \nand Homogenization  \nAI Actor Tasks:  AI Deployment, AI Impact Assessment, Domain Experts, End -Users, Operation and Monitoring, TEVV  \n \nMEASURE 2.10:  Privacy risk of the AI system – as identified in the MAP function – is examined and documented.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.10- 001 Conduct AI red -teaming to assess issues  such as: Outputting of training data',
    '30 MEASURE 2.2:  Evaluations involving human subjects meet applicable requirements (including human subject protection) and are \nrepresentative of the relevant population.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.2-001 Assess and manage statistical biases related to GAI content provenance through \ntechniques such as re -sampling, re -weighting, or adversarial training.  Information Integrity ; Information \nSecurity ; Harmful Bias and \nHomogenization  \nMS-2.2-002 Document how content provenance data  is tracked  and how that data interact s \nwith  privacy and security . Consider : Anonymiz ing data to protect the privacy of \nhuman subjects; Leverag ing privacy output filters; Remov ing any personally',
]
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]

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.8
cosine_accuracy@3 0.99
cosine_accuracy@5 0.99
cosine_accuracy@10 1.0
cosine_precision@1 0.8
cosine_precision@3 0.33
cosine_precision@5 0.198
cosine_precision@10 0.1
cosine_recall@1 0.8
cosine_recall@3 0.99
cosine_recall@5 0.99
cosine_recall@10 1.0
cosine_ndcg@10 0.9195
cosine_mrr@10 0.8917
cosine_map@100 0.8917
dot_accuracy@1 0.8
dot_accuracy@3 0.99
dot_accuracy@5 0.99
dot_accuracy@10 1.0
dot_precision@1 0.8
dot_precision@3 0.33
dot_precision@5 0.198
dot_precision@10 0.1
dot_recall@1 0.8
dot_recall@3 0.99
dot_recall@5 0.99
dot_recall@10 1.0
dot_ndcg@10 0.9195
dot_mrr@10 0.8917
dot_map@100 0.8917

Training Details

Training Dataset

Unnamed Dataset

  • Size: 600 training samples
  • Columns: sentence_0 and sentence_1
  • Approximate statistics based on the first 600 samples:
    sentence_0 sentence_1
    type string string
    details
    • min: 13 tokens
    • mean: 21.67 tokens
    • max: 34 tokens
    • min: 3 tokens
    • mean: 132.86 tokens
    • max: 512 tokens
  • Samples:
    sentence_0 sentence_1
    What is the title of the NIST publication related to Artificial Intelligence Risk Management? NIST Trustworthy and Responsible AI
    NIST AI 600 -1
    Artificial Intelligence Risk Management
    Framework: Generative Artificial
    Intelligence Profile


    This publication is available free of charge from:
    https://doi.org/10.6028/NIST.AI.600 -1
    Where can the NIST AI 600 -1 publication be accessed for free? NIST Trustworthy and Responsible AI
    NIST AI 600 -1
    Artificial Intelligence Risk Management
    Framework: Generative Artificial
    Intelligence Profile


    This publication is available free of charge from:
    https://doi.org/10.6028/NIST.AI.600 -1
    What is the title of the publication released by NIST in July 2024 regarding artificial intelligence? NIST Trustworthy and Responsible AI
    NIST AI 600 -1
    Artificial Intelligence Risk Management
    Framework: Generative Artificial
    Intelligence Profile


    This publication is available free of charge from:
    https://doi.org/10.6028/NIST.AI.600 -1

    July 2024




    U.S. Department of Commerce
    Gina M. Raimondo, Secretary
    National Institute of Standards and Technology
    Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology
  • Loss: MatryoshkaLoss 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: 20
  • per_device_eval_batch_size: 20
  • num_train_epochs: 5
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • 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

Training Logs

Epoch Step cosine_map@100
1.0 30 0.8722
1.6667 50 0.8817
2.0 60 0.8867
3.0 90 0.8867
3.3333 100 0.8917

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.1.0
  • Transformers: 4.44.2
  • PyTorch: 2.4.0+cu121
  • Accelerate: 0.34.2
  • Datasets: 2.19.2
  • 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}
}
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