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 dimensions
- 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': 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("vijayarulmuthu/finetuned_arctic_ft-a85433c9-6284-4afb-8e87-e110823d565c")
# Run inference
sentences = [
'Whom did David smite and subdue, taking Gath and her towns from their control?',
'Now after this it came to pass, that David smote the Philistines, and subdued them, and took Gath and her towns out of the hand of the Philistines. And he smote Moab; and the Moabites became David’s servants, [and] brought gifts. And David smote Hadarezer king of Zobah unto Hamath, as he went to stablish his dominion by the river Euphrates. And David took from him a thousand chariots, and seven thousand horsemen, and twenty thousand footmen: David also houghed all the chariot [horses], but reserved of them an hundred chariots. And when the Syrians of Damascus came to help Hadarezer king of Zobah, David slew of the Syrians two and twenty thousand men. Then David put [garrisons] in Syriadamascus; and the Syrians became David’s servants, [and] brought gifts. Thus the LORD preserved David whithersoever he went. And David took the shields of gold that were on the servants of Hadarezer, and brought them to Jerusalem. Likewise from Tibhath, and from Chun, cities of Hadarezer, brought David very much brass, wherewith Solomon made the brasen sea, and the pillars, and the vessels of brass.',
'So Shishak king of Egypt came up against Jerusalem, and took away the treasures of the house of the LORD, and the treasures of the king’s house; he took all: he carried away also the shields of gold which Solomon had made. Instead of which king Rehoboam made shields of brass, and committed [them] to the hands of the chief of the guard, that kept the entrance of the king’s house. And when the king entered into the house of the LORD, the guard came and fetched them, and brought them again into the guard chamber. And when he humbled himself, the wrath of the LORD turned from him, that he would not destroy [him] altogether: and also in Judah things went well. So king Rehoboam strengthened himself in Jerusalem, and reigned: for Rehoboam [was] one and forty years old when he began to reign, and he reigned seventeen years in Jerusalem, the city which the LORD had chosen out of all the tribes of Israel, to put his name there. And his mother’s name [was] Naamah an Ammonitess. And he did evil, because he prepared not his heart to seek the LORD. Now the acts of Rehoboam, first and last, [are] they not written in the book of Shemaiah the prophet, and of Iddo the seer concerning genealogies? And [there were] wars between Rehoboam and Jeroboam continually. And Rehoboam slept with his fathers, and was buried in the city of David: and Abijah his son reigned in his stead.',
]
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
- Dataset:
validation
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6479 |
cosine_accuracy@3 | 0.833 |
cosine_accuracy@5 | 0.873 |
cosine_accuracy@10 | 0.9238 |
cosine_precision@1 | 0.6479 |
cosine_precision@3 | 0.2777 |
cosine_precision@5 | 0.1746 |
cosine_precision@10 | 0.0924 |
cosine_recall@1 | 0.018 |
cosine_recall@3 | 0.0231 |
cosine_recall@5 | 0.0242 |
cosine_recall@10 | 0.0257 |
cosine_ndcg@10 | 0.1739 |
cosine_mrr@10 | 0.7469 |
cosine_map@100 | 0.0208 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 6,612 training samples
- Columns:
sentence_0
andsentence_1
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 type string string details - min: 7 tokens
- mean: 17.56 tokens
- max: 42 tokens
- min: 13 tokens
- mean: 250.95 tokens
- max: 504 tokens
- Samples:
sentence_0 sentence_1 What was the reason given by Elijah the prophet for the LORD's punishment on Jehoram?
Then Jehoram went forth with his princes, and all his chariots with him: and he rose up by night, and smote the Edomites which compassed him in, and the captains of the chariots. So the Edomites revolted from under the hand of Judah unto this day. The same time [also] did Libnah revolt from under his hand; because he had forsaken the LORD God of his fathers. Moreover he made high places in the mountains of Judah, and caused the inhabitants of Jerusalem to commit fornication, and compelled Judah [thereto]. And there came a writing to him from Elijah the prophet, saying, Thus saith the LORD God of David thy father, Because thou hast not walked in the ways of Jehoshaphat thy father, nor in the ways of Asa king of Judah, But hast walked in the way of the kings of Israel, and hast made Judah and the inhabitants of Jerusalem to go a whoring, like to the whoredoms of the house of Ahab, and also hast slain thy brethren of thy father’s house, [which were] better than thyself: Behold, with a gre...
What happened at the sixth hour until the ninth hour according to the passage?
And we indeed justly; for we receive the due reward of our deeds: but this man hath done nothing amiss. And he said unto Jesus, Lord, remember me when thou comest into thy kingdom. And Jesus said unto him, Verily I say unto thee, To day shalt thou be with me in paradise. And it was about the sixth hour, and there was a darkness over all the earth until the ninth hour. And the sun was darkened, and the veil of the temple was rent in the midst. And when Jesus had cried with a loud voice, he said, Father, into thy hands I commend my spirit: and having said thus, he gave up the ghost. Now when the centurion saw what was done, he glorified God, saying, Certainly this was a righteous man. And all the people that came together to that sight, beholding the things which were done, smote their breasts, and returned.
Who is commanded by the Lord to set a watchman and declare what he sees?
The burden of the desert of the sea. As whirlwinds in the south pass through; [so] it cometh from the desert, from a terrible land. A grievous vision is declared unto me; the treacherous dealer dealeth treacherously, and the spoiler spoileth. Go up, O Elam: besiege, O Media; all the sighing thereof have I made to cease. Therefore are my loins filled with pain: pangs have taken hold upon me, as the pangs of a woman that travaileth: I was bowed down at the hearing [of it]; I was dismayed at the seeing [of it]. My heart panted, fearfulness affrighted me: the night of my pleasure hath he turned into fear unto me. Prepare the table, watch in the watchtower, eat, drink: arise, ye princes, [and] anoint the shield. For thus hath the Lord said unto me, Go, set a watchman, let him declare what he seeth. And he saw a chariot [with] a couple of horsemen, a chariot of asses, [and] a chariot of camels; and he hearkened diligently with much heed: And he cried, A lion: My lord, I stand continually upo...
- 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
: stepsper_device_train_batch_size
: 10per_device_eval_batch_size
: 10num_train_epochs
: 10multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 10per_device_eval_batch_size
: 10per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Click to expand
Epoch | Step | Training Loss | validation_cosine_ndcg@10 |
---|---|---|---|
0.0755 | 50 | - | 0.0982 |
0.1511 | 100 | - | 0.1408 |
0.2266 | 150 | - | 0.1546 |
0.3021 | 200 | - | 0.1612 |
0.3776 | 250 | - | 0.1655 |
0.4532 | 300 | - | 0.1663 |
0.5287 | 350 | - | 0.1710 |
0.6042 | 400 | - | 0.1704 |
0.6798 | 450 | - | 0.1713 |
0.7553 | 500 | 2.378 | 0.1702 |
0.8308 | 550 | - | 0.1727 |
0.9063 | 600 | - | 0.1734 |
0.9819 | 650 | - | 0.1741 |
1.0 | 662 | - | 0.1745 |
1.0574 | 700 | - | 0.1752 |
1.1329 | 750 | - | 0.1761 |
1.2085 | 800 | - | 0.1750 |
1.2840 | 850 | - | 0.1719 |
1.3595 | 900 | - | 0.1730 |
1.4350 | 950 | - | 0.1760 |
1.5106 | 1000 | 0.7402 | 0.1776 |
1.5861 | 1050 | - | 0.1757 |
1.6616 | 1100 | - | 0.1774 |
1.7372 | 1150 | - | 0.1757 |
1.8127 | 1200 | - | 0.1749 |
1.8882 | 1250 | - | 0.1745 |
1.9637 | 1300 | - | 0.1758 |
2.0 | 1324 | - | 0.1776 |
2.0393 | 1350 | - | 0.1772 |
2.1148 | 1400 | - | 0.1751 |
2.1903 | 1450 | - | 0.1757 |
2.2659 | 1500 | 0.467 | 0.1742 |
2.3414 | 1550 | - | 0.1748 |
2.4169 | 1600 | - | 0.1738 |
2.4924 | 1650 | - | 0.1749 |
2.5680 | 1700 | - | 0.1772 |
2.6435 | 1750 | - | 0.1772 |
2.7190 | 1800 | - | 0.1772 |
2.7946 | 1850 | - | 0.1774 |
2.8701 | 1900 | - | 0.1770 |
2.9456 | 1950 | - | 0.1757 |
3.0 | 1986 | - | 0.1771 |
3.0211 | 2000 | 0.2653 | 0.1762 |
3.0967 | 2050 | - | 0.1745 |
3.1722 | 2100 | - | 0.1748 |
3.2477 | 2150 | - | 0.1749 |
3.3233 | 2200 | - | 0.1766 |
3.3988 | 2250 | - | 0.1746 |
3.4743 | 2300 | - | 0.1749 |
3.5498 | 2350 | - | 0.1766 |
3.6254 | 2400 | - | 0.1752 |
3.7009 | 2450 | - | 0.1749 |
3.7764 | 2500 | 0.1809 | 0.1746 |
3.8520 | 2550 | - | 0.1751 |
3.9275 | 2600 | - | 0.1755 |
4.0 | 2648 | - | 0.1744 |
4.0030 | 2650 | - | 0.1747 |
4.0785 | 2700 | - | 0.1747 |
4.1541 | 2750 | - | 0.1766 |
4.2296 | 2800 | - | 0.1761 |
4.3051 | 2850 | - | 0.1745 |
4.3807 | 2900 | - | 0.1748 |
4.4562 | 2950 | - | 0.1753 |
4.5317 | 3000 | 0.1368 | 0.1741 |
4.6073 | 3050 | - | 0.1718 |
4.6828 | 3100 | - | 0.1730 |
4.7583 | 3150 | - | 0.1735 |
4.8338 | 3200 | - | 0.1753 |
4.9094 | 3250 | - | 0.1744 |
4.9849 | 3300 | - | 0.1752 |
5.0 | 3310 | - | 0.1758 |
5.0604 | 3350 | - | 0.1771 |
5.1360 | 3400 | - | 0.1758 |
5.2115 | 3450 | - | 0.1741 |
5.2870 | 3500 | 0.1178 | 0.1741 |
5.3625 | 3550 | - | 0.1746 |
5.4381 | 3600 | - | 0.1744 |
5.5136 | 3650 | - | 0.1740 |
5.5891 | 3700 | - | 0.1743 |
5.6647 | 3750 | - | 0.1744 |
5.7402 | 3800 | - | 0.1733 |
5.8157 | 3850 | - | 0.1747 |
5.8912 | 3900 | - | 0.1755 |
5.9668 | 3950 | - | 0.1734 |
6.0 | 3972 | - | 0.1740 |
6.0423 | 4000 | 0.0878 | 0.1745 |
6.1178 | 4050 | - | 0.1734 |
6.1934 | 4100 | - | 0.1725 |
6.2689 | 4150 | - | 0.1748 |
6.3444 | 4200 | - | 0.1743 |
6.4199 | 4250 | - | 0.1742 |
6.4955 | 4300 | - | 0.1738 |
6.5710 | 4350 | - | 0.1756 |
6.6465 | 4400 | - | 0.1746 |
6.7221 | 4450 | - | 0.1754 |
6.7976 | 4500 | 0.0697 | 0.1756 |
6.8731 | 4550 | - | 0.1755 |
6.9486 | 4600 | - | 0.1755 |
7.0 | 4634 | - | 0.1755 |
7.0242 | 4650 | - | 0.1752 |
7.0997 | 4700 | - | 0.1766 |
7.1752 | 4750 | - | 0.1745 |
7.2508 | 4800 | - | 0.1751 |
7.3263 | 4850 | - | 0.1746 |
7.4018 | 4900 | - | 0.1747 |
7.4773 | 4950 | - | 0.1742 |
7.5529 | 5000 | 0.0643 | 0.1743 |
7.6284 | 5050 | - | 0.1736 |
7.7039 | 5100 | - | 0.1739 |
7.7795 | 5150 | - | 0.1737 |
7.8550 | 5200 | - | 0.1736 |
7.9305 | 5250 | - | 0.1744 |
8.0 | 5296 | - | 0.1750 |
8.0060 | 5300 | - | 0.1751 |
8.0816 | 5350 | - | 0.1742 |
8.1571 | 5400 | - | 0.1739 |
8.2326 | 5450 | - | 0.1745 |
8.3082 | 5500 | 0.0521 | 0.1745 |
8.3837 | 5550 | - | 0.1746 |
8.4592 | 5600 | - | 0.1743 |
8.5347 | 5650 | - | 0.1744 |
8.6103 | 5700 | - | 0.1750 |
8.6858 | 5750 | - | 0.1749 |
8.7613 | 5800 | - | 0.1748 |
8.8369 | 5850 | - | 0.1747 |
8.9124 | 5900 | - | 0.1747 |
8.9879 | 5950 | - | 0.1746 |
9.0 | 5958 | - | 0.1746 |
9.0634 | 6000 | 0.044 | 0.1745 |
9.1390 | 6050 | - | 0.1742 |
9.2145 | 6100 | - | 0.1740 |
9.2900 | 6150 | - | 0.1742 |
9.3656 | 6200 | - | 0.1744 |
9.4411 | 6250 | - | 0.1739 |
9.5166 | 6300 | - | 0.1737 |
9.5921 | 6350 | - | 0.1740 |
9.6677 | 6400 | - | 0.1738 |
9.7432 | 6450 | - | 0.1739 |
9.8187 | 6500 | 0.043 | 0.1738 |
9.8943 | 6550 | - | 0.1738 |
9.9698 | 6600 | - | 0.1739 |
10.0 | 6620 | - | 0.1739 |
Framework Versions
- Python: 3.13.3
- Sentence Transformers: 4.1.0
- Transformers: 4.52.3
- PyTorch: 2.7.0
- Accelerate: 1.7.0
- 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}
}
- Downloads last month
- 45
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support
Model tree for vijayarulmuthu/finetuned_arctic_ft-a85433c9-6284-4afb-8e87-e110823d565c
Base model
Snowflake/snowflake-arctic-embed-mEvaluation results
- Cosine Accuracy@1 on validationself-reported0.648
- Cosine Accuracy@3 on validationself-reported0.833
- Cosine Accuracy@5 on validationself-reported0.873
- Cosine Accuracy@10 on validationself-reported0.924
- Cosine Precision@1 on validationself-reported0.648
- Cosine Precision@3 on validationself-reported0.278
- Cosine Precision@5 on validationself-reported0.175
- Cosine Precision@10 on validationself-reported0.092
- Cosine Recall@1 on validationself-reported0.018
- Cosine Recall@3 on validationself-reported0.023