This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("tjohn327/scion-minilm-l6-v3")
# Run inference
sentences = [
    'How many active ASes are reported as of the CIDR report mentioned in the document?',
    '<title>Pervasive Internet-Wide Low-Latency Authentication</title>\n<section>C. AS as Opportunistically Trusted Entity</section>\n<content>\nEach entity in the Internet is part of at least one AS, which is under the control of a single administrative entity. This facilitates providing a common service that authenticates endpoints (e.g., using a challenge-response protocol or preinstalled keys and certificates) and issues certificates. Another advantage is the typically close relationship between an endpoint and its AS, which allows for a stronger leverage in case of misbehavior. Since it is infeasible for an endpoint to authenticate each AS by itself (there are ∼71 000 active ASes according to the CIDR report  [4] ), RPKI is used as a trust anchor to authenticate ASes. RPKI resource issuers assign an AS a set of IP address prefixes that this AS is allowed to originate. An AS then issues short-lived certificates for its authorized IP address ranges.\n</content>',
    '<title>Unknown Title</title>\n<section>\uf735.\uf731 Paths emission per unit of traffic</section>\n<content>\nThe reason is that the number of BGP paths is less than \uf735 for most AS pairs. This figure also suggests that the \uf735-greenest paths average emission differs from the greenest path emission and the n-greenest paths average emission for both beaconing algorithms. However, for every percentile, this difference in SCI-GIB is about \uf733 times less than the one in SCI-BCE. This means that the \uf735-greenest paths average emission in SCI-GIB is much closer to the greenest path emission than SCI-BCE. Also, for every percentile, the difference between the \uf735-greenest paths average emissions of the two different beaconing algorithms is \uf732 times more than the difference between their greenest path emissions. From both of these observations, we conclude that SCI-GIB is better at finding the greenest set of paths\n</content>',
]
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

Information Retrieval

Dataset: val-ir-eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6294
cosine_accuracy@3	0.8216
cosine_accuracy@5	0.8764
cosine_accuracy@10	0.9309
cosine_precision@1	0.6294
cosine_precision@3	0.2739
cosine_precision@5	0.1755
cosine_precision@10	0.0933
cosine_recall@1	0.6292
cosine_recall@3	0.821
cosine_recall@5	0.8759
cosine_recall@10	0.9306
cosine_ndcg@10	0.7828
cosine_mrr@10	0.7351
cosine_map@100	0.7379

Training Details

Training Dataset

Unnamed Dataset

Size: 46,618 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 2 tokens
mean: 21.15 tokens
max: 45 tokens
min: 86 tokens
mean: 200.21 tokens
max: 256 tokens

Samples:

sentence_0	sentence_1
`What specific snippet of the resolver-recv-answer-for-client rule is presented in the document?`	`[object Object][object Object][object Object][object Object][object Object]`
`What is the relationship between early adopters and the potential security improvements mentioned for SBAS in the document?`	`[object Object][object Object][object Object][object Object][object Object]`
`How does the evaluation in this study focus on user-driven path control within SCION?`	`[object Object][object Object][object Object][object Object][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: 64
per_device_eval_batch_size: 64
fp16: True
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: 64
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: 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: 3
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: 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: 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
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
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	val-ir-eval_cosine_ndcg@10
0.1372	100	-	0.6950
0.2743	200	-	0.7313
0.4115	300	-	0.7443
0.5487	400	-	0.7573
0.6859	500	0.3862	0.7576
0.8230	600	-	0.7627
0.9602	700	-	0.7662
1.0	729	-	0.7709
1.0974	800	-	0.7705
1.2346	900	-	0.7718
1.3717	1000	0.2356	0.7747
1.5089	1100	-	0.7742
1.6461	1200	-	0.7759
1.7833	1300	-	0.7776
1.9204	1400	-	0.7807
2.0	1458	-	0.7815
2.0576	1500	0.1937	0.7789
2.1948	1600	-	0.7814
2.3320	1700	-	0.7819
2.4691	1800	-	0.7823
2.6063	1900	-	0.7827
2.7435	2000	0.1758	0.7828

Framework Versions

Python: 3.12.3
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0+cu124
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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}
}

	sentence_0	sentence_1
type	string	string
details	min: 2 tokens mean: 21.15 tokens max: 45 tokens	min: 86 tokens mean: 200.21 tokens max: 256 tokens