This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-xs on the sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3 dataset. It maps sentences & paragraphs to a 384-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-xs
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
Language: en
License: apache-2.0

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': 384, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("krumeto/snowflake-arctic-embed-xs-ms-marco-triplet")
# Run inference
sentences = [
    'Represent this sentence for searching relevant passages: how many pitchers used per game',
    'In modern day baseball, teams generally have five starting pitchers, and they take it in turn to start a game every fifth day (hence the phrase rotation). Sometimes, if the schedule pans out, a team can get away with a four man rotation, and in the distant past some teams managed a three man rotation.',
    'Accroding to the statistics in the Baseball Reference page showing 2014 Major League Baseball Pitching Pitches, 745 pitchers threw 704,983 pitches in 2430 games for an average of 290 pitches per game.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

Dataset: xs-msmarco-triplet
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.571
dot_accuracy	0.4286
manhattan_accuracy	0.5728
euclidean_accuracy	0.571
max_accuracy	0.5728

Triplet

Dataset: xs-msmarco-triplet-train
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.5696
dot_accuracy	0.43
manhattan_accuracy	0.5674
euclidean_accuracy	0.5696
max_accuracy	0.5696

Training Details

Training Dataset

sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3

Dataset: sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
Size: 100,000 training samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 12 tokens
mean: 17.05 tokens
max: 36 tokens
min: 19 tokens
mean: 78.68 tokens
max: 212 tokens
min: 16 tokens
mean: 73.73 tokens
max: 205 tokens

Samples:

query	positive	negative
`Represent this sentence for searching relevant passages: what are the liberal arts?`	`liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.`	`Liberal Arts Defined. The liberal arts are a set of academic disciplines that include the sciences and the humanities. When you study a liberal arts curriculum, you don't have to have one specific career goal, although you might. Instead, you'll assemble a broad foundation of knowledge that can be used in a wide spectrum of careers.`
`Represent this sentence for searching relevant passages: what is the mechanism of action of fibrinolytic or thrombolytic drugs?`	`BailliÃ¨re's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.`	`Fibrinolytic drug. Fibrinolytic drug, also called thrombolytic drug, any agent that is capable of stimulating the dissolution of a blood clot (thrombus). Fibrinolytic drugs work by activating the so-called fibrinolytic pathway.`
`Represent this sentence for searching relevant passages: what is normal plat count`	78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).	`In the context of blood work, PLT refers to the platelet count. Platelets are the cells that cause blood clotting and control bleeding. The normal range of platelets for adults is 3.5 to 10.5 billion cells per liter of blood, according to the Mayo Clinic. Continue Reading.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3

Dataset: sentence-transformers/msmarco-msmarco-mini_lm-l-6-v3
Size: 5,000 evaluation samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 12 tokens
mean: 17.09 tokens
max: 35 tokens
min: 21 tokens
mean: 80.07 tokens
max: 250 tokens
min: 16 tokens
mean: 76.73 tokens
max: 341 tokens

Samples:

query	positive	negative
`Represent this sentence for searching relevant passages: what is the name of the material harder than diamonds`	`Nano-material is harder than diamonds By Will Knight A material that is harder than diamond has been created in the lab, by packing together tiny ânanorodsâ of carbon. The new material, known as aggregated carbon nanorods (ACNR), was created by compressing and heating super-strong carbon molecules called buckyballs or carbon-60.`	`What material is stronger than diamond? Diamonds are famous for their hardness, but not particularly for strength,since they are easily cleaved to create the facets that make the different cuts of diamonds for setting in a ring or necklace. Many materials are stronger than diamonds, only a couple of synthesized materials are harder than diamonds.`
`Represent this sentence for searching relevant passages: is pink impression a perennial tulip?`	`Tulip Pink Impression. close video. VIDEO. Tulip Pink Impression. The rich pink blooms of this hybrid are bound to make an impression, whether used in the landscape or as a cut flower. Robust stems and giant blooms characterise the range, and this hybrid is no exception. âPink Impressionâ will continue to impress thanks to the perennial potential of this range.`	`Tulip Pink Impression. The lustrous petals are a deep, rich rose at the center, shading to a delicate pale pink at the edge, while doing amazing things in between that include shades of both melon and sunset. Tall, strong, long-lasting and reliable, like most Darwin hybrids. An absolutely first-class Tulip.`
`Represent this sentence for searching relevant passages: define: colonization`	`Colonization. the settlement and economic development of the uninhabited borderlands of a country (internal colonization) or the establishment of settlements (engaging primarily in agricultural activity) beyond the frontiers of a country (external colonization).`	`Colonization is a process by which a central system of power dominates the surrounding land and its components. The term is derived from the Latin word colere, which means to inhabit. Also, colonization refers strictly to migration, for example, to settler colonies in America or Australia, trading posts, and plantations, while colonialism deals with this, along with ruling the existing indigenous peoples of styled new territories. Colonization was linked to the spread of tens of millions fro`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: 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: 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
learning_rate: 5e-05
weight_decay: 0.0
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: 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: True
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	xs-msmarco-triplet-train_max_accuracy	xs-msmarco-triplet_max_accuracy
0	0	-	-	-	0.3648
0.016	100	1.0213	1.0078	-	0.3672
0.032	200	0.9785	0.9630	-	0.3764
0.048	300	0.9591	0.9190	-	0.4014
0.064	400	0.9088	0.8906	-	0.4392
0.08	500	0.9215	0.8707	-	0.4598
0.096	600	0.8692	0.8681	-	0.4874
0.112	700	0.8806	0.8538	-	0.4964
0.128	800	0.8801	0.8477	-	0.5106
0.144	900	0.8692	0.8414	-	0.5228
0.16	1000	0.8624	0.8391	-	0.5194
0.176	1100	0.8737	0.8397	-	0.5264
0.192	1200	0.8505	0.8344	-	0.5214
0.208	1300	0.8818	0.8358	-	0.5164
0.224	1400	0.8464	0.8269	-	0.5326
0.24	1500	0.8623	0.8291	-	0.5232
0.256	1600	0.8203	0.8407	-	0.5328
0.272	1700	0.8566	0.8257	-	0.5302
0.288	1800	0.8386	0.8198	-	0.5364
0.304	1900	0.8587	0.8172	-	0.5388
0.32	2000	0.8472	0.8233	-	0.5568
0.336	2100	0.8466	0.8188	-	0.5468
0.352	2200	0.8273	0.8190	-	0.5386
0.368	2300	0.8356	0.8183	-	0.5396
0.384	2400	0.8294	0.8156	-	0.5538
0.4	2500	0.8274	0.8168	-	0.5448
0.416	2600	0.8392	0.8093	-	0.5422
0.432	2700	0.8541	0.8087	-	0.5426
0.448	2800	0.8218	0.8086	-	0.5474
0.464	2900	0.8446	0.8062	-	0.554
0.48	3000	0.8405	0.8076	-	0.548
0.496	3100	0.8447	0.8087	-	0.553
0.512	3200	0.8453	0.8073	-	0.5536
0.528	3300	0.8371	0.8089	-	0.5504
0.544	3400	0.8548	0.8005	-	0.5516
0.56	3500	0.8162	0.8026	-	0.5572
0.576	3600	0.8577	0.7994	-	0.5558
0.592	3700	0.8289	0.7990	-	0.5526
0.608	3800	0.824	0.7967	-	0.562
0.624	3900	0.833	0.7959	-	0.5608
0.64	4000	0.8362	0.7958	-	0.5554
0.656	4100	0.8057	0.7966	-	0.5578
0.672	4200	0.8001	0.7943	-	0.5646
0.688	4300	0.8215	0.7937	-	0.5602
0.704	4400	0.8257	0.7933	-	0.5614
0.72	4500	0.8173	0.7942	-	0.5648
0.736	4600	0.8002	0.7922	-	0.5698
0.752	4700	0.8445	0.7899	-	0.5626
0.768	4800	0.825	0.7897	-	0.5592
0.784	4900	0.8151	0.7870	-	0.5696
0.8	5000	0.8223	0.7895	-	0.5676
0.816	5100	0.8235	0.7877	-	0.5656
0.832	5200	0.8355	0.7866	-	0.5688
0.848	5300	0.8218	0.7864	-	0.5672
0.864	5400	0.8384	0.7866	-	0.5652
0.88	5500	0.7988	0.7860	-	0.569
0.896	5600	0.8117	0.7867	-	0.5684
0.912	5700	0.8113	0.7861	-	0.5734
0.928	5800	0.8129	0.7860	-	0.5698
0.944	5900	0.799	0.7863	-	0.5688
0.96	6000	0.8269	0.7858	-	0.5708
0.976	6100	0.8066	0.7857	-	0.572
0.992	6200	0.8302	0.7856	-	0.5728
1.0	6250	-	-	0.5696	-

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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}
}

	query	positive	negative
type	string	string	string
details	min: 12 tokens mean: 17.05 tokens max: 36 tokens	min: 19 tokens mean: 78.68 tokens max: 212 tokens	min: 16 tokens mean: 73.73 tokens max: 205 tokens