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khoj-ai
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timely-arctic-medium

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:55744
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
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Technical Report and Model Pipeline

To access our technical report and model pipeline scripts visit our github

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Represent this sentence for searching relevant passages: Witki, Warmian-Masurian Voivodeship 2040 Oct 12',
    'Witki () is a village in the administrative district of Gmina Bartoszyce, within Bartoszyce County, Warmian-Masurian Voivodeship, in northern Poland, close to the border with the Kaliningrad Oblast of Russia. It lies approximately east of Bartoszyce and north-east of the regional capital Olsztyn. References Witki 12/10/2040\n',
    '12-21-2046 This is a list of electoral results for the Electoral district of Irwin in Western Australian state elections. Members for Irwin Election results Elections in the 1940s  Preferences were not distributed.  Preferences were not distributed. Elections in the 1930s  Preferences were not distributed. Elections in the 1920s Elections in the 1910s Elections in the 1900s Elections in the 1890s References Western Australian state electoral results by district\n',
]
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]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 55,744 training samples
  • Columns: anchors and positive
  • Approximate statistics based on the first 1000 samples:
    anchors positive
    type string string
    details
    • min: 14 tokens
    • mean: 20.33 tokens
    • max: 33 tokens
    • min: 17 tokens
    • mean: 48.58 tokens
    • max: 75 tokens
  • Samples:
    anchors positive
    Represent this sentence for searching relevant passages: are bugs attracted to citronella November 10? Citronella is naturally occurring oil that repels insects. ... “Citronella oil is repellent to mosquitoes to a degree, but the amount being put out by a candle isn't going to be very effective,” Eric Hoffer, president of Hoffer Pest, told TODAY Home. 11/10
    Represent this sentence for searching relevant passages: are bugs attracted to citronella 11/10/09? Citronella is naturally occurring oil that repels insects. ... “Citronella oil is repellent to mosquitoes to a degree, but the amount being put out by a candle isn't going to be very effective,” Eric Hoffer, president of Hoffer Pest, told TODAY Home. 11/10/09
    Represent this sentence for searching relevant passages: are bugs attracted to citronella Jan 15? Citronella is naturally occurring oil that repels insects. ... “Citronella oil is repellent to mosquitoes to a degree, but the amount being put out by a candle isn't going to be very effective,” Eric Hoffer, president of Hoffer Pest, told TODAY Home. 01/15
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

  • Size: 1,000 evaluation samples
  • Columns: anchors and positive
  • Approximate statistics based on the first 1000 samples:
    anchors positive
    type string string
    details
    • min: 12 tokens
    • mean: 21.57 tokens
    • max: 43 tokens
    • min: 8 tokens
    • mean: 66.44 tokens
    • max: 512 tokens
  • Samples:
    anchors positive
    Represent this sentence for searching relevant passages: Identify a Psychopath 3/28 Psychopathy is a personality construct consisting of a cluster of characteristics used by mental health professionals to describe someone who is charming, manipulative, emotionally ruthless and potentially criminal. 03/28
    Represent this sentence for searching relevant passages: what is dangerous high blood pressure in pregnancy? A blood pressure that is greater than 130/90 mm Hg or that is 15 degrees higher on the top number from where you started before pregnancy may be cause for concern. High blood pressure during pregnancy is defined as 140 mm Hg or higher systolic, with diastolic 90 mm Hg or higher.
    Represent this sentence for searching relevant passages: Be a Better Cheerleader June 22 What do you think when you think of a good cheerleader? Tight with motions? Can hold a stunt? Well, it's not just that. You need to be fit in 3 categories: mental/emotional health, social health, and physical health. 06/22
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 128
  • per_device_eval_batch_size: 64
  • learning_rate: 1.5e-05
  • weight_decay: 0.01
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • warmup_steps: 400
  • bf16: True
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 128
  • per_device_eval_batch_size: 64
  • 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: 1.5e-05
  • weight_decay: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 1
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 400
  • 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: True
  • 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: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss loss
0.0023 1 2.4713 -
0.0229 10 2.4907 -
0.0459 20 2.4574 -
0.0688 30 2.4861 -
0.0917 40 2.4612 -
0.1147 50 2.4353 -
0.1376 60 2.3967 -
0.1606 70 2.3609 -
0.1835 80 2.3079 -
0.2064 90 2.1928 -
0.2294 100 2.1581 -
0.2523 110 2.0822 -
0.2752 120 1.9739 -
0.2982 130 1.8393 -
0.3211 140 1.7397 -
0.3440 150 1.5249 -
0.3670 160 1.4281 -
0.3899 170 1.3197 -
0.4128 180 1.211 -
0.4358 190 1.1086 -
0.4587 200 0.9598 0.2301
0.4817 210 1.0904 -
0.5046 220 0.9813 -
0.5275 230 1.1148 -
0.5505 240 1.2813 -
0.5734 250 1.2259 -
0.5963 260 1.221 -
0.6193 270 1.1547 -
0.6422 280 1.1286 -
0.6651 290 0.9932 -
0.6881 300 0.978 -
0.7110 310 0.9505 -
0.7339 320 0.8731 -
0.7569 330 0.824 -
0.7798 340 0.8979 -
0.8028 350 1.756 -
0.8257 360 1.6785 -
0.8486 370 1.5944 -
0.8716 380 1.5417 -
0.8945 390 1.4788 -
0.9174 400 0.9873 0.0695
0.9404 410 0.1664 -
0.9633 420 0.1336 -
0.9862 430 0.1193 -

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.43.4
  • PyTorch: 2.4.0+cu121
  • Accelerate: 0.33.0
  • Datasets: 2.20.0
  • 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",
}

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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