This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-s. 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-s
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Represent this sentence for searching relevant passages: Philadelphia Business Journal 01/30/83',
    'The Philadelphia Business Journal is a diversified business media company in Philadelphia, Pennsylvania, publishing daily stories on its website and social networks, and a weekly edition available in print and online. It is published by the American City Business Journals. See also List of newspapers in Pennsylvania References External links Business newspapers published in the United States Newspapers published in Philadelphia Jan 30 1983\n',
    "Have you just bought your brand new Nintendo Wii console? Are you gutted that you can't get Wii Connect 24 in your country of residence? This article will resolve this problem, so you can surf the Internet on your Wii!\n",
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 55,736 training samples
Columns: anchors and positive
Approximate statistics based on the first 1000 samples:
anchors positive
type string string
details
min: 14 tokens
mean: 20.25 tokens
max: 33 tokens
min: 15 tokens
mean: 47.2 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[object Object]`
`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[object Object]`
`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[object Object]`

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

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: 11 tokens
mean: 21.67 tokens
max: 39 tokens
min: 15 tokens
mean: 67.0 tokens
max: 512 tokens

Samples:

anchors	positive
`Represent this sentence for searching relevant passages: 4/23 Socialize With Someone Who Is Losing Interest in You`	`04/23 It can hurt to realize that someone you care about is losing interest in you. If a friend has stopped calling you and no longer makes plans to hang out, your first instinct might be to contact them more frequently or to ignore them in return.[object Object]`
`Represent this sentence for searching relevant passages: Alathur taluk, Perambalur 04/19/29`	`Alathur taluk is a taluk in Perambalur district in the Indian state of Tamil Nadu. It was created by former chief minister J.Jayalalithaa for issues of population increase. Kunnam taluk was bifurcated to form this new taluk. Villages There are 39 villages in Alathur taluk excluding the headquarters Alathur. References Perambalur district Taluks of Perambalur district 2029 Apr 19[object Object]`
`Represent this sentence for searching relevant passages: 01/04 how much weight does a baby gain in the first month?`	`01/04 During their first month, most newborns gain weight at a rate of about 1 ounce (30 grams) per day. They generally grow in height about 1 to 1½ inches (2.54 to 3.81 centimeters) during the first month. Many newborns go through a period of rapid growth when they are 7 to 10 days old and again at 3 and 6 weeks.[object Object]`

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: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 1
warmup_ratio: 0.1
warmup_steps: 400
bf16: True
torch_compile: True
torch_compile_backend: inductor
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: 128
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: 2e-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: True
torch_compile_backend: inductor
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	2.3154	-
0.0229	10	2.3237	-
0.0459	20	2.4036	-
0.0688	30	2.3314	-
0.0917	40	2.3171	-
0.1147	50	2.2891	-
0.0023	1	2.2343	-
0.0229	10	2.2256	-
0.0459	20	2.2924	-
0.0688	30	2.2354	-
0.0917	40	2.2281	-
0.1147	50	2.2018	-
0.1376	60	2.2377	-
0.1606	70	2.2001	-
0.1835	80	2.158	-
0.2064	90	2.1405	-
0.2294	100	2.0916	-
0.2523	110	2.0374	-
0.2752	120	2.0492	-
0.2982	130	1.9824	-
0.3211	140	1.9571	-
0.3440	150	1.8317	-
0.3670	160	1.7183	-
0.3899	170	1.5928	-
0.4128	180	1.5695	-
0.4358	190	1.4592	-
0.4587	200	1.2667	0.2031
0.4817	210	1.3865	-
0.5046	220	1.2924	-
0.5275	230	1.3042	-
0.5505	240	1.4393	-
0.5734	250	1.3402	-
0.5963	260	1.1939	-
0.6193	270	1.1795	-
0.6422	280	1.1012	-
0.6651	290	1.0379	-
0.6881	300	0.9865	-
0.7110	310	0.9088	-
0.7339	320	0.9132	-
0.7569	330	0.8819	-
0.7798	340	0.8631	-
0.8028	350	1.4084	-
0.8257	360	1.325	-
0.8486	370	1.2373	-
0.8716	380	1.1881	-
0.8945	390	1.1656	-
0.9174	400	0.7767	0.0607
0.9404	410	0.1511	-
0.9633	420	0.1439	-
0.9862	430	0.1216	-

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.43.3
PyTorch: 2.4.0+cu121
Accelerate: 0.33.0
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}
}

	anchors	positive
type	string	string
details	min: 14 tokens mean: 20.25 tokens max: 33 tokens	min: 15 tokens mean: 47.2 tokens max: 75 tokens

khoj-ai/timely-arctic-small

Technical Report and Model Pipeline

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

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

khoj-ai/
timely-arctic-small