kamkol/ab_testing_finetuned_2_arctic_ft-711c8143-04b7-4ecb-8e01-8847274c1d9c

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 d8fb21ca8d905d2832ee8b96c894d3298964346b -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 1024 dimensions
• —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("kamkol/ab_testing_finetuned_2_arctic_ft-711c8143-04b7-4ecb-8e01-8847274c1d9c")
# Run inference
sentences = [
    'What are the estimated monthly rates of first-party cookie deletion among U.S. Internet users according to comScore and Anirban Dasgupta et al.?',
    'Tracking on the Web: Privacy and Security Implication [23]. \nCookie deletion rates are hard to estimate.  Using a panel \nof 400,000 home PCs, comScore estimated that 31 percent of \nU.S. Internet users cleared their first -party cookies during a \nmonth [24].   Anirban Dasgupta etal. [25] showed similar \nlevels of cookie clearing (25% -33% monthly, depending on \ngeography) based on the Yahoo toolbar.  Such rates imply \nthat long studies (e.g., months), where users are identified \nbased on cookies could have a selection bi as problem – as',
    'both directions: when we show fewer ads, users increase \nengagement, and when we show more ads, users decrease \nengagement or abandon. If users abandon at different rates \nbetween Control and Treatment, the remaining surviving \npopulation is different, and the conclusions can be \ncompletely wrong.  For example, the users generating the \nmost revenue may get annoyed with more ads and abandon, \nleaving a surviving population with lower Revenue/user. \n3. Not taking into account selection bias [34].  \n In an online longitudinal study that relies on cookies for \nidentification, there is likely to be a significant attrition due',
]
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]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Information Retrieval

• —Evaluated with <code>InformationRetrievalEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 250 training samples
• —Columns: <code>sentence0</code> and <code>sentence1</code>
• —Approximate statistics based on the first 250 samples: | | sentence0 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 24.08 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 123.64 tokens</li><li>max: 151 tokens</li></ul> |
• —Samples: | sentence0 | sentence1 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What are some common pitfalls associated with long-term online controlled experiments as discussed by Dmitriev et al. in their IEEE Big Data paper?</code> | <code>Appears in IEEE Big Data. Paper available at http://bit.ly/expLongTerm <br> <br>Pitfalls of Long-Term Online Controlled Experiments <br>Pavel Dmitriev, Brian Frasca, Somit Gupta, Ron Kohavi, Garnet Vaz <br>Analysis and Experimentation <br>Microsoft Corporation <br>Redmond, WA 98052, USA <br>{padmitri,brianfra,sogupta,ronnyk,gavaz}@microsoft.com <br> <br> <br>Abstract—Online controlled experiments (e.g., A/B tests) are <br>now regularly used to guide product development and <br>accelerate innovation in software. Product ideas are evaluated</code> | | <code>What are online controlled experiments used for in product development according to the context?</code> | <code>Analysis and Experimentation <br>Microsoft Corporation <br>Redmond, WA 98052, USA <br>{padmitri,brianfra,sogupta,ronnyk,gavaz}@microsoft.com <br> <br> <br>Abstract—Online controlled experiments (e.g., A/B tests) are <br>now regularly used to guide product development and <br>accelerate innovation in software. Product ideas are evaluated <br>as scientific hypotheses, and tested on web sit es, mobile <br>applications, desktop applications, services, and operating <br>system features. <br>One of the key challenges for organizations that run <br>controlled experiments is to select an Overall Evaluation</code> | | <code>What is one of the key challenges organizations face when running online controlled experiments?</code> | <code>Abstract—Online controlled experiments (e.g., A/B tests) are <br>now regularly used to guide product development and <br>accelerate innovation in software. Product ideas are evaluated <br>as scientific hypotheses, and tested on web sit es, mobile <br>applications, desktop applications, services, and operating <br>system features. <br>One of the key challenges for organizations that run <br>controlled experiments is to select an Overall Evaluation <br>Criterion (OEC), i.e., the criterion by which to evaluat e the <br>different variants. The difficulty is that short -term changes to <br>metrics may not predict the long-term impact of a change. For</code> |
• —Loss: <code>MatryoshkaLoss</code> with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128
      ],
      "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: 100
• —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: 100
• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —tp_size: 0
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: None
• —hub_always_push: False
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —include_inputs_for_metrics: False
• —include_for_metrics: []
• —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
• —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
• —use_liger_kernel: False
• —eval_use_gather_object: False
• —average_tokens_across_devices: False
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: round_robin

</details>

Training Logs

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

</details>

Framework Versions

• —Python: 3.11.12
• —Sentence Transformers: 4.1.0
• —Transformers: 4.51.3
• —PyTorch: 2.6.0+cu124
• —Accelerate: 1.6.0
• —Datasets: 3.6.0
• —Tokenizers: 0.21.1

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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