Edit model card

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("vincha77/finetuned_arctic")
# Run inference
sentences = [
    'What rights do applicants have if their application for credit is denied according to the CFPB?',
    'information in their credit report." The CFPB has also asserted that "[t]he law gives every applicant the right to \na specific explanation if their application for credit was denied, and that right is not diminished simply because \na company uses a complex algorithm that it doesn\'t understand."92 Such explanations illustrate a shared value \nthat certain decisions need to be explained. \nA California law requires that warehouse employees are provided with notice and explana-\ntion about quotas, potentially facilitated by automated systems, that apply to them. Warehous-\ning employers in California that use quota systems (often facilitated by algorithmic monitoring systems) are \nrequired to provide employees with a written description of each quota that applies to the employee, including \n“quantified number of tasks to be performed or materials to be produced or handled, within the defined',
    'even if the inferences are not accurate (e.g., confabulations), and especially if they reveal information \nthat the individual considers sensitive or that is used to disadvantage or harm them. \nBeyond harms from information exposure (such as extortion or dignitary harm), wrong or inappropriate \ninferences of PII can contribute to downstream or secondary harmful impacts. For example, predictive \ninferences made by GAI models based on PII or protected attributes can contribute to adverse decisions, \nleading to representational or allocative harms to individuals or groups (see Harmful Bias and \nHomogenization below).',
]
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 0.98
cosine_accuracy@3 1.0
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.98
cosine_precision@3 0.3333
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.98
cosine_recall@3 1.0
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9913
cosine_mrr@10 0.9883
cosine_map@100 0.9883
dot_accuracy@1 0.98
dot_accuracy@3 1.0
dot_accuracy@5 1.0
dot_accuracy@10 1.0
dot_precision@1 0.98
dot_precision@3 0.3333
dot_precision@5 0.2
dot_precision@10 0.1
dot_recall@1 0.98
dot_recall@3 1.0
dot_recall@5 1.0
dot_recall@10 1.0
dot_ndcg@10 0.9913
dot_mrr@10 0.9883
dot_map@100 0.9883

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: 12 tokens
    • mean: 21.21 tokens
    • max: 39 tokens
    • min: 21 tokens
    • mean: 182.02 tokens
    • max: 512 tokens
  • Samples:
    sentence_0 sentence_1
    What are the responsibilities of AI Actors in monitoring reported issues related to GAI system performance? 45
    MG-4.1-007
    Verify that AI Actors responsible for monitoring reported issues can effectively
    evaluate GAI system performance including the application of content
    provenance data tracking techniques, and promptly escalate issues for response.
    Human-AI Configuration;
    Information Integrity
    AI Actor Tasks: AI Deployment, Affected Individuals and Communities, Domain Experts, End-Users, Human Factors, Operation and
    Monitoring

    MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular
    engagement with interested parties, including relevant AI Actors.
    Action ID
    Suggested Action
    GAI Risks
    MG-4.2-001 Conduct regular monitoring of GAI systems and publish reports detailing the
    performance, feedback received, and improvements made.
    Harmful Bias and Homogenization
    MG-4.2-002
    Practice and follow incident response plans for addressing the generation of
    How are measurable activities for continual improvements integrated into AI system updates according to the context provided? 45
    MG-4.1-007
    Verify that AI Actors responsible for monitoring reported issues can effectively
    evaluate GAI system performance including the application of content
    provenance data tracking techniques, and promptly escalate issues for response.
    Human-AI Configuration;
    Information Integrity
    AI Actor Tasks: AI Deployment, Affected Individuals and Communities, Domain Experts, End-Users, Human Factors, Operation and
    Monitoring

    MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular
    engagement with interested parties, including relevant AI Actors.
    Action ID
    Suggested Action
    GAI Risks
    MG-4.2-001 Conduct regular monitoring of GAI systems and publish reports detailing the
    performance, feedback received, and improvements made.
    Harmful Bias and Homogenization
    MG-4.2-002
    Practice and follow incident response plans for addressing the generation of
    What is the main function of the app discussed in Samantha Cole's article from June 26, 2019? them
    10. Samantha Cole. This Horrifying App Undresses a Photo of Any Woman With a Single Click. Motherboard.
    June 26, 2019. https://www.vice.com/en/article/kzm59x/deepnude-app-creates-fake-nudes-of-any-woman
    11. Lauren Kaori Gurley. Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make.
    Motherboard. Sep. 20, 2021. https://www.vice.com/en/article/88npjv/amazons-ai-cameras-are-punishing­
    drivers-for-mistakes-they-didnt-make
    63
  • 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: 16
  • per_device_eval_batch_size: 16
  • 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: 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: 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 38 0.965
1.3158 50 0.9783
2.0 76 0.9767
2.6316 100 0.9833
3.0 114 0.9883

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

@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}
}
Downloads last month
10
Safetensors
Model size
109M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for vincha77/finetuned_arctic

Finetuned
(29)
this model

Space using vincha77/finetuned_arctic 1

Evaluation results