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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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': 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("chelleboyer/llm-evals-2-a56b96e9-5b1a-4351-9b07-3c46a9e2bfe6")
# Run inference
sentences = [
    'What is the expression for the minimum variance of \\( z^{\\mathsf{cv};\\alpha} \\) in terms of \\(\\rho\\) and \\(\\mathrm{Var}[z]\\)?',
    'minα∈ℝ\u2061Var\u2062[z𝖼𝗏;α]=(1−ρ2)\u2062Var\u2062[z].subscript𝛼ℝVardelimited-[]superscript𝑧𝖼𝗏𝛼1superscript𝜌2Vardelimited-[]𝑧\\displaystyle\\min_{\\alpha\\in\\mathbb{R}}\\mathrm{Var}[z^{\\mathsf{cv};\\alpha}]=%\n\\left(1-\\rho^{2}\\right)\\mathrm{Var}[z].roman_min start_POSTSUBSCRIPT italic_α ∈ blackboard_R end_POSTSUBSCRIPT roman_Var [ italic_z start_POSTSUPERSCRIPT sansserif_cv ; italic_α end_POSTSUPERSCRIPT ] = ( 1 - italic_ρ start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ) roman_Var [ italic_z ] .\n\n\n\nThe minimum is achieved if and only if α𝛼\\alphaitalic_α equals',
    'explored how to select these components or how their different combinations influence the results.',
]
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]

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.94
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.94
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.94
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9752
cosine_mrr@10	0.9667
cosine_map@100	0.9667

Training Details

Training Dataset

Unnamed Dataset

Size: 782 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 782 samples:
sentence_0 sentence_1
type string string
details
min: 5 tokens
mean: 31.75 tokens
max: 178 tokens
min: 3 tokens
mean: 148.03 tokens
max: 309 tokens

Samples:

sentence_0	sentence_1
`What role do control variates play in accelerating unbiased LLM evaluation as discussed in the context?`	Accelerating Unbiased LLM Evaluation via Synthetic Feedback[object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object]1 Introduction[object Object][object Object]2 Related Work[object Object][object Object]2.1 LLM Evaluation: Metric, Benchmark and Systems[object Object]2.2 Speeding Up LLM Evaluation[object Object]2.3 Control Variates, Application, and related techniques[object Object][object Object][object Object][object Object]3 Preliminaries[object Object][object Object]3.1 LLM Evaluation[object Object]3.2 Human and Synthetic Evaluation[object Object]3.3 Other Notations[object Object][object Object][object Object][object Object]4 Efficient LLM Evaluation via Control Variates[object Object][object Object][object Object]4.1 Control Variates[object Object][object Object]Human annotation saving ratio.[object Object][object Object][object Object][object Object]4.2 Control Variates Evaluation
`How does the concept of human annotation saving ratio relate to the use of control variates in efficient LLM evaluation?`	Accelerating Unbiased LLM Evaluation via Synthetic Feedback[object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object]1 Introduction[object Object][object Object]2 Related Work[object Object][object Object]2.1 LLM Evaluation: Metric, Benchmark and Systems[object Object]2.2 Speeding Up LLM Evaluation[object Object]2.3 Control Variates, Application, and related techniques[object Object][object Object][object Object][object Object]3 Preliminaries[object Object][object Object]3.1 LLM Evaluation[object Object]3.2 Human and Synthetic Evaluation[object Object]3.3 Other Notations[object Object][object Object][object Object][object Object]4 Efficient LLM Evaluation via Control Variates[object Object][object Object][object Object]4.1 Control Variates[object Object][object Object]Human annotation saving ratio.[object Object][object Object][object Object][object Object]4.2 Control Variates Evaluation
`What are the key steps involved in the Control Variates Evaluation process as outlined in the context?`	4.2 Control Variates Evaluation[object Object][object Object]Synthetic annotation gathering (Line 4).[object Object]Human annotation sampling (Line 5).[object Object]Synthetic win rate estimation (Line 6).[object Object]Control variates coefficient computation (Line 7).[object Object]Win rate estimation (Line 8).[object Object](Optional) Synthetic evaluator finetuning (Line 3).[object Object]Summary.[object Object][object Object][object Object][object Object][object Object][object Object]5 Experiments[object Object][object Object][object Object]5.1 Setup[object Object][object Object]Synthetic evaluators.[object Object]Finetuning procedure.[object Object]Benchmark.[object Object][object Object][object Object][object Object]5.2 Control Variates Evaluation v.s. Human Evaluation[object Object][object Object]Human annotation saving ratio on different benchmarks and synthetic evaluators.[object Object]Theory matches practice.

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 5
per_device_eval_batch_size: 5
num_train_epochs: 10
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 5
per_device_eval_batch_size: 5
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: 10
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: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
tp_size: 0
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: 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

Training Logs

Epoch	Step	Training Loss	cosine_ndcg@10
0.3185	50	-	0.9539
0.6369	100	-	0.9826
0.9554	150	-	0.9726
1.0	157	-	0.9852
1.2739	200	-	0.9826
1.5924	250	-	0.9826
1.9108	300	-	0.9826
2.0	314	-	0.9826
2.2293	350	-	0.9752
2.5478	400	-	0.9852
2.8662	450	-	0.9852
3.0	471	-	0.9852
3.1847	500	0.3143	0.9752
3.5032	550	-	0.9752
3.8217	600	-	0.9852
4.0	628	-	0.9852
4.1401	650	-	0.9779
4.4586	700	-	0.9826
4.7771	750	-	0.9852
5.0	785	-	0.9852
5.0955	800	-	0.9852
5.4140	850	-	0.9852
5.7325	900	-	0.9826
6.0	942	-	0.9779
6.0510	950	-	0.9779
6.3694	1000	0.0878	0.9852
6.6879	1050	-	0.9779
7.0	1099	-	0.9852
7.0064	1100	-	0.9852
7.3248	1150	-	0.9852
7.6433	1200	-	0.9852
7.9618	1250	-	0.9852
8.0	1256	-	0.9852
8.2803	1300	-	0.9852
8.5987	1350	-	0.9826
8.9172	1400	-	0.9852
9.0	1413	-	0.9852
9.2357	1450	-	0.9826
9.5541	1500	0.0422	0.9826
9.8726	1550	-	0.9752
10.0	1570	-	0.9752

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: 2.14.4
Tokenizers: 0.21.1

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",
}

MatryoshkaLoss

bibtex

@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

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}
}

	sentence_0	sentence_1
type	string	string
details	min: 5 tokens mean: 31.75 tokens max: 178 tokens	min: 3 tokens mean: 148.03 tokens max: 309 tokens