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("Mercity/memory-retrieval-minilm-l6-v2")
# Run inference
sentences = [
    'Provide the amortization schedule difference for CC1 under both prioritization schemas.',
    "Mark's primary long-term goal is to start his own small business within the next four years, requiring significant capital savings to begin operations.",
    "Last spring, Dr. Vasquez received high praise from the Dean for successfully transitioning her entire curriculum to align with a newly adopted, highly structured 'Systems Thinking Framework' mandated by the university board.",
]
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,  0.9104, -0.3007],
#         [ 0.9104,  1.0000, -0.2945],
#         [-0.3007, -0.2945,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 370,023 training samples
Columns: sentence_0, sentence_1, and sentence_2
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2
type string string string
details
min: 11 tokens
mean: 31.57 tokens
max: 76 tokens
min: 17 tokens
mean: 36.18 tokens
max: 64 tokens
min: 13 tokens
mean: 32.21 tokens
max: 154 tokens

Samples:

sentence_0	sentence_1	sentence_2
`Yoga by the sea for Nina's soul? Add scenic drives that whisper sweet nothings.`	`Nina developed a strong, vocal aversion to driving manual transmission cars after a stressful rental experience in Tuscany in 2018.`	`Sofia recently shared that her grandmother, who is a major influencer in the family, is recovering from a minor surgery and requires quiet, low-stress environments for the next few months.`
`Targeting analysis of 200 sites to sway city green space policies; realistic under these limits?`	`Dr. Patel has a strong, established working relationship with the City Planning Department head, who is personally invested in seeing the pollinator data incorporated into the 2026 municipal budget review.`	`Alex reviewed the seed funding agreement which stipulated that 10% ($50,000) of the capital must be reserved solely for executive bonuses upon Series A closing.`
`Gluten-free options avoiding cross-contamination in our small kitchen? I truly value this.`	`Lisa has a very close relationship with her neighbor, Sarah, who is a professional chef specializing in South American cuisine and often offers to help prep on Sundays.`	`Alex's agent advised varying sentence structure with occasional future tense projections during character betrayals to heighten emotional stakes in the plot.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.COSINE", 3 "triplet_margin": 0.5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 32
per_device_eval_batch_size: 32
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
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	Training Loss
0.0432	500	0.3372
0.0865	1000	0.1651
0.1297	1500	0.1505
0.1730	2000	0.1367
0.2162	2500	0.128
0.2594	3000	0.1213
0.3027	3500	0.1167
0.3459	4000	0.1087
0.3891	4500	0.1017
0.4324	5000	0.1019
0.4756	5500	0.096
0.5189	6000	0.0963
0.5621	6500	0.0883
0.6053	7000	0.0879
0.6486	7500	0.0865
0.6918	8000	0.0822
0.7350	8500	0.0815
0.7783	9000	0.0784
0.8215	9500	0.0754
0.8648	10000	0.0767
0.9080	10500	0.0732
0.9512	11000	0.0713
0.9945	11500	0.0677
1.0	11564	-
1.0377	12000	0.0611
1.0809	12500	0.0572
1.1242	13000	0.0596
1.1674	13500	0.0576
1.2107	14000	0.0562
1.2539	14500	0.0544
1.2971	15000	0.0543
1.3404	15500	0.0544
1.3836	16000	0.0533
1.4268	16500	0.0515
1.4701	17000	0.0501
1.5133	17500	0.0515
1.5566	18000	0.0494
1.5998	18500	0.0495
1.6430	19000	0.0477
1.6863	19500	0.0471
1.7295	20000	0.0478
1.7727	20500	0.0453
1.8160	21000	0.0447
1.8592	21500	0.0463
1.9025	22000	0.0462
1.9457	22500	0.0445
1.9889	23000	0.0436
2.0	23128	-
2.0322	23500	0.0376
2.0754	24000	0.0377
2.1186	24500	0.0364
2.1619	25000	0.0381
2.2051	25500	0.0382
2.2484	26000	0.0359
2.2916	26500	0.036
2.3348	27000	0.0365
2.3781	27500	0.0362
2.4213	28000	0.036
2.4645	28500	0.0359
2.5078	29000	0.0364
2.5510	29500	0.0355
2.5943	30000	0.0354
2.6375	30500	0.0348
2.6807	31000	0.0362
2.7240	31500	0.0345
2.7672	32000	0.0351
2.8104	32500	0.0345
2.8537	33000	0.0351
2.8969	33500	0.0339
2.9402	34000	0.0354
2.9834	34500	0.0356
3.0	34692	-

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu128
Accelerate: 1.11.0
Datasets: 4.4.0
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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 11 tokens mean: 31.57 tokens max: 76 tokens	min: 17 tokens mean: 36.18 tokens max: 64 tokens	min: 13 tokens mean: 32.21 tokens max: 154 tokens