This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased on the msmarco dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: distilbert/distilbert-base-uncased
Maximum Sequence Length: 300 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Dot Product
Supported Modality: Text
Training Dataset:
- msmarco
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'DistilBertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)

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("kwondw/distilbert-base-uncased-msmarco-margin-mse")
# Run inference
queries = [
    'average pay of it analyst in austin texas',
]
documents = [
    'How much does an IT Analyst in Austin, TX make? The average IT Analyst salary in Austin, TX is approximately $26.79 per hour, which is 26% below the national average. Salary information comes from 76 data points collected directly from employees, users, and past and present job advertisements on Indeed in the past 24 months.',
    'The best city between Austin, TX and Santa Fe, NM to meet is Lubbock, Texas which is about 34 miles from the exact midpoint. The town that marks the exact halfway point is actually Close City, Texas.',
    'Food and cosmetic use. Arrowroot powder is a natural powdered root starch that is white in color and very lightweight. It is derived from the tropical South American plant Maranta arundinacea. The plant was given the name â\x80\x9cArrowrootâ\x80\x9d because it was once used to treat those injured with wounds from poisoned arrows.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[46.6321, 42.2351, 35.4387]])

Evaluation

Metrics

Information Retrieval

Dataset: NanoMSMARCO
Evaluated with InformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.02
dot_accuracy@3	0.04
dot_accuracy@5	0.08
dot_accuracy@10	0.14
dot_precision@1	0.02
dot_precision@3	0.0133
dot_precision@5	0.016
dot_precision@10	0.014
dot_recall@1	0.02
dot_recall@3	0.04
dot_recall@5	0.08
dot_recall@10	0.14
dot_ndcg@10	0.0683
dot_mrr@10	0.0469
dot_map@100	0.0689

Training Details

Training Dataset

msmarco

Dataset: msmarco
Size: 100 training samples
Columns: score, query, positive, and negative

Approximate statistics based on the first 100 samples:

	score	query	positive	negative
type	float	string	string	string
details	min: 0.52 mean: 14.12 max: 22.35	min: 5 tokens mean: 9.28 tokens max: 20 tokens	min: 27 tokens mean: 86.96 tokens max: 282 tokens	min: 23 tokens mean: 71.84 tokens max: 146 tokens

Samples:

score	query	positive	negative
`18.26768692334493`	`weisskopf syndrome`	`Dinno-Shearer-Weisskopf syndrome: Introduction. Dinno-Shearer-Weisskopf syndrome: A very rare syndrome characterized mainly by long limbs, tall stature, large head, ataxia and facial anomalies. More detailed information about the symptoms, causes, and treatments of Dinno-Shearer-Weisskopf syndrome is available below.`	`Related posts: 1 Charles Bonnet Syndrome - Treatment, Symptoms and Causes. 2 Refeeding Syndrome - Symptoms, Causes, Diagnosis, Treatment Guidelines. 3 Dubowitz Syndrome.`
`21.609440167744957`	`weather in beirut, lebanon`	`Beirut: Annual Weather Averages. August is the hottest month in Beirut with an average temperature of 28Â°C (82Â°F) and the coldest is February at 13Â°C (55Â°F) with the most daily sunshine hours at 14 in July. The wettest month is January with an average of 130mm of rain.`	`When Brant and his Mohawk warriors joined the fortunes. of the Johnson family in Canada, the influence of President. Whelock, of Lebanon Institute, where Brant had received his. early education, was brought to bear upon the crisis.`
`15.881475607554119`	`what are the clots in my period`	`Blood Clots during Period. It is normal to pass blood clots during menses, as along with the blood the thickened lining of the uterus is also being shed and expelled outside the body. In case the abnormal blood clots during period occur, visit your gynecologist as it may then be an indication of other serious medical illnesses.`	`Heparin lock flush is used for: Preventing blood clots from forming in intravenous (IV) lines or catheters. Heparin lock flush is an anticoagulant. It works by preventing blood clots from forming in intravenous lines when they are not in use. Do NOT use heparin lock flush if:`

Loss: MarginMSELoss with these parameters:
json
1{ 2 "similarity_fct": "pairwise_dot_score" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 1
warmup_steps: 0.1
fp16: True

All Hyperparameters

Click to expand

do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0.1
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: True
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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
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
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_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
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
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	NanoMSMARCO_dot_ndcg@10
-1	-1	-	0.0683
1.0	1	186.3345	0.0683
-1	-1	-	0.0683

Training Time

Training: 8.7 seconds
Evaluation: 3.5 seconds
Total: 12.2 seconds

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.4.1
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 5.0.0
Tokenizers: 0.22.2

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",
}

MarginMSELoss

bibtex

@misc{hofstätter2021improving,
    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
    year={2021},
    eprint={2010.02666},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}