The hard-negative bag-of-documents reranker: all-MiniLM-L6-v2 fine-tuned with MultipleNegativesRankingLoss on bag-derived pairs mined with FAISS hard negatives (Amazon ESCI). Part of the bag-of-documents retrieval research; see the live demo.

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

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 = [
    'course coffee grinder',
    'Bodum BISTRO Blade Grinder, Electric Blade Coffee Grinder, Black',
    'Hamilton Beach Fresh Grind Electric Coffee Grinder for Beans, Spices and More, Stainless Steel Blades, Removable Chamber, Makes up to 12 Cups, Black',
]
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, 0.6150, 0.5448],
#         [0.6150, 1.0000, 0.7990],
#         [0.5448, 0.7990, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 337,969 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: 3 tokens
mean: 6.52 tokens
max: 23 tokens
min: 4 tokens
mean: 29.41 tokens
max: 86 tokens
min: 3 tokens
mean: 23.74 tokens
max: 73 tokens

Samples:

sentence_0	sentence_1	sentence_2
`mens sweater burgundy medium`	`Shephe 4 Ply Men's V Neck Cashmere Sweater Burgundy XXX-Large`	`Amazon Essentials Men's Standard Crewneck Sweater, Burgundy, XS`
`shoe covers`	`Yueiehe 5 Pairs (10 Pieces) Multi-Function Dust Duster Mop Slippers Shoes Cover, Soft Washable Reusable Microfiber Foot Socks Floor Cleaning Tools Shoe Cover`	`Nirohee Silicone Shoes Covers, Shoe Covers, Rain Boots Reusable Easy to Carry for Women, Men, Kids. (Black, L)`
`wrench organizer drawer`	`ABN Magnetic Wrench Organizer - 4 Piece Tool Box Drawer Organizer Tray Wrench Rack Magnetic Tool Box Accessories`	`Whitmor 6-Section Clear Drawer Organizer`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

per_device_train_batch_size: 64
num_train_epochs: 3
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1
label_smoothing_factor: 0.0
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: no
per_device_eval_batch_size: 64
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: 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
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0947	500	1.9500
0.1894	1000	1.5351
0.2840	1500	1.4213
0.3787	2000	1.3370
0.4734	2500	1.2762
0.5681	3000	1.2182
0.6628	3500	1.1795
0.7574	4000	1.1409
0.8521	4500	1.1297
0.9468	5000	1.0969
1.0415	5500	1.0346
1.1361	6000	0.9986
1.2308	6500	0.9855
1.3255	7000	0.9591
1.4202	7500	0.9544
1.5149	8000	0.9485
1.6095	8500	0.9268
1.7042	9000	0.9330
1.7989	9500	0.9160
1.8936	10000	0.9020
1.9883	10500	0.8954
2.0829	11000	0.8412
2.1776	11500	0.8448
2.2723	12000	0.8289
2.3670	12500	0.8343
2.4617	13000	0.8356
2.5563	13500	0.8208
2.6510	14000	0.8272
2.7457	14500	0.8042
2.8404	15000	0.8145
2.9351	15500	0.8171

Framework Versions

Python: 3.14.4
Sentence Transformers: 5.3.0
Transformers: 5.4.0
PyTorch: 2.11.0
Accelerate: 1.13.0
Datasets: 4.8.4
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",
}

MultipleNegativesRankingLoss