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 retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

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': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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 = [
    'Noise Pop buds with 50 Hrs of playtime, Quad Mic with ENC Bluetooth',
    'Noise Pop Buds Truly Wireless Bluetooth Earbuds, Lilac Pop',
    'Realme Buds T110 with AI ENC for calls, upto 38 hours of Playback and Fast Charging Bluetooth Headset (Jazz Blue, True Wireless)',
]
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, 0.6913, 0.4763],
#         [0.6913, 1.0000, 0.3947],
#         [0.4763, 0.3947, 1.0000]])

Evaluation

Metrics

Binary Classification

Dataset: dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.8
cosine_accuracy_threshold	0.9176
cosine_f1	0.6538
cosine_f1_threshold	0.6996
cosine_precision	0.5
cosine_recall	0.9444
cosine_ap	0.7089
cosine_mcc	0.4991

Training Details

Training Dataset

Unnamed Dataset

Size: 36 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 36 samples:
sentence_0 sentence_1
type string string
details
min: 7 tokens
mean: 30.17 tokens
max: 62 tokens
min: 14 tokens
mean: 40.92 tokens
max: 65 tokens

Samples:

sentence_0	sentence_1
`Mivi Duopods N4 True Wireless TWS Earbuds, Upto 50 hrs of playtime, 13mm driver, Bluetooth v5.3, Dual Connection, Voice Assistance, Bluetooth v5.3, AI ENC, Low Latency, Cobalt Black`	`Mivi Duopods N4 True Wireless TWS Earbuds, Upto 50 hrs of playtime, 13mm driver, Bluetooth v5.3, Dual Connection, Voice Assistance, Bluetooth v5.3, AI ENC, Low Latency, Lime White`
`boAt Airdopes 191 ANC in-ear earbuds with 60 Hrs Playtime, 4 Mics with ENx Tech, 32 dB ANC, Ambient Mode, Seamless In-ear detection, 13 mm drivers & Bluetooth v5.3 (Pellucid Orange)`	`boAt Airdopes 191 ANC in-ear earbuds with 60 Hrs Playtime, 4 Mics with ENx Tech, 32 dB ANC, Ambient Mode, Seamless In-ear detection, 13 mm drivers & Bluetooth v5.3 (Pellucid White)`
`Mivi Duopods N5 True Wireless TWS Earbuds, Upto 50 hrs of Playtime, bluetooth v5.3, Low Latency, AI ENC, 13mm Driver, Ivory Sheen`	`Mivi Duopods N5 True Wireless TWS Earbuds, Upto 50 hrs of Playtime, bluetooth v5.3, Low Latency, AI ENC, 13mm Driver, Lush Green`

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: 32
per_device_eval_batch_size: 32
multi_dataset_batch_sampler: round_robin

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
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0
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: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
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: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	dev_cosine_ap
1.0	1	0.6912
2.0	2	0.7085
3.0	3	0.7089

Training Time

Training: 4.0 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",
}

MultipleNegativesRankingLoss