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, 'architecture': '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'superconductivity',
    'We highlight the reproducibility and level of control over the electrical\nproperties of YBa$_2$Cu$_3$O$_7$ Josephson junctions fabricated with\nirradiation from a focused helium ion beam. Specifically, we show the results\nof electrical transport properties for several junctions fabricated using a\nlarge range of irradiation doses. At the lower end of this range, junctions\nexhibit superconductor-normal metal-superconductor (SNS) Josephson junction\nproperties. However, as dose increases there is a transition to electrical\ncharacteristics consistent with superconductor-insulator-superconductor (SIS)\njunctions. To investigate the uniformity of large numbers of helium ion\nJosephson junctions we fabricate arrays of both SNS and SIS Josephson junctions\ncontaining 20 connected in series. Electrical transport properties for these\narrays reveal very uniform junctions with no appreciable spread in critical\ncurrent or resistance.',
    'Non-invasive magnetic field sensing using optically - detected magnetic\nresonance of nitrogen-vacancy (NV) centers in diamond was used to study spatial\ndistribution of the magnetic induction upon penetration and expulsion of weak\nmagnetic fields in several representative superconductors. Vector magnetic\nfields were measured on the surface of conventional, Pb and Nb, and\nunconventional, LuNi$_2$B$_2$C, Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$,\nBa(Fe$_{0.93}$Co$_{0.07}$)$_2$As$_2$, and CaKFe$_4$As$_4$, superconductors,\nwith diffraction - limited spatial resolution using variable - temperature\nconfocal system. Magnetic induction profiles across the crystal edges were\nmeasured in zero-field-cooled (ZFC) and field-cooled (FC) conditions. While all\nsuperconductors show nearly perfect screening of magnetic fields applied after\ncooling to temperatures well below the superconducting transition, $T_c$, a\nrange of very different behaviors was observed for Meissner expulsion upon\ncooling in static magnetic field from above $T_c$. Substantial conventional\nMeissner expulsion is found in LuNi$_2$B$_2$C, paramagnetic Meissner effect\n(PME) is found in Nb, and virtually no expulsion is observed in iron-based\nsuperconductors. In all cases, good correlation with macroscopic measurements\nof total magnetic moment is found. Our measurements of the spatial distribution\nof magnetic induction provide insight into microscopic physics of the Meissner\neffect.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3505, 0.3544],
#         [0.3505, 1.0000, 0.6777],
#         [0.3544, 0.6777, 1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: superconductor-eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7
cosine_accuracy@3	0.8
cosine_accuracy@5	0.9
cosine_accuracy@10	1.0
cosine_precision@1	0.7
cosine_precision@3	0.3667
cosine_precision@5	0.24
cosine_precision@10	0.13
cosine_recall@1	0.5
cosine_recall@3	0.7
cosine_recall@5	0.8
cosine_recall@10	0.9
cosine_ndcg@10	0.7651
cosine_mrr@10	0.7861
cosine_map@100	0.7098

Training Details

Training Dataset

Unnamed Dataset

Size: 4,091 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: 3 tokens
mean: 7.98 tokens
max: 41 tokens
min: 8 tokens
mean: 160.85 tokens
max: 256 tokens

Samples:

sentence_0	sentence_1
`codimension two lump solutions in string field theory and ta`	`We present some solutions for lumps in two dimensions in level-expanded[object Object]string field theory, as well as in two tachyonic theories: pure tachyonic[object Object]string field theory and pure $\phi^3$ theory. Much easier to handle, these[object Object]theories might be used to help understanding solitonic features of string field[object Object]theory. We compare lump solutions between these theories and we discuss some[object Object]convergence issues.`
`superconductivity explained`	We review the current understanding of superconductivity in the[object Object]quasi-one-dimensional organic conductors of the Bechgaard and Fabre salt[object Object]families. We discuss the interplay between superconductivity,[object Object]antiferromagnetism, and charge-density-wave fluctuations. The connection to[object Object]recent experimental observations supporting unconventional pairing and the[object Object]possibility of a triplet-spin order parameter for the superconducting phase is[object Object]also presented.
`erez berg`	Erez Berg- Theory of Strange Metals \| Understanding "strange metal" phenomena - metallic behavior that deviates from that expected of an ordinary Fermi liquid down to the lowest measurable temperatures - is among the most puzzling open problems in condensed matter physics. Such phenomena are observed across many different strongly correlated materials. They seem tied to other interesting phenomena, such as quantum criticality and unconventional superconductivity. I will describe theoretical advances in understanding the possible or

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
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: 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
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: 4
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
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}
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	superconductor-eval_cosine_ndcg@10
1.0	256	0.7651

Framework Versions

Python: 3.9.6
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0
Accelerate: 1.10.1
Datasets: 4.3.0
Tokenizers: 0.22.1

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