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}) with Transformer model: 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})
)

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("manupande21/all-MiniLM-L6-v2-finetuned-triplets_2M")
# Run inference
sentences = [
    'do employer rrsp contributions count as income',
    'up vote 0 down vote accepted. Yes, the extra matching contribution your employer puts into your group RRSP plan is considered employment income and so yes it would be included in the income reported on your T4.owever, you should also receive from your RRSP plan administrator a contribution receipt, and the amount on that receipt should include both your contributions and the $500.',
    '1 Although, higher tax rates may apply to your taxable contributions if you are a high income earner (ie income over $300,000). 2  Salary sacrifice can lower your taxable income When you salary sacrifice, your employer makes the extra contribution before income tax is taken out.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

Dataset: test-eval
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9828

Training Details

Training Dataset

Unnamed Dataset

Size: 1,600,000 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: 4 tokens
mean: 9.07 tokens
max: 35 tokens
min: 18 tokens
mean: 78.28 tokens
max: 187 tokens
min: 19 tokens
mean: 76.02 tokens
max: 220 tokens

Samples:

sentence_0	sentence_1	sentence_2
`Factor analysis is a statistical procedure that can be used to`	Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.For example, it is possible that variations in four observed variables mainly reflect the variations in two unobserved variables.xploratory factor analysis (EFA) is used to identify complex interrelationships among items and group items that are part of unified concepts. The researcher makes no a priori assumptions about relationships among factors.	`When it comes to data analysis, some believe that statistical techniques are only applicable for quantitative data. This is not so. There are many statistical techniques that can be applied to qualitative data, such as ratings scales, that has been generated by a quantitative research approach.`
`what county is northridge ca in`	`Northridge, CA. Northridge is located in south California. Northridge is part of Los Angeles County. On average, the public school district that covers Northridge is worse than the state average in quality. The Northridge area code is 818.`	`Find Rialto, CA clerk, including county, city, and circuit clerk, and clerk of court. Clerks x California x San Bernardino County x Rialto x.`
`where is denso auto parts made`	`Welcome. One of DENSO's largest international automotive operations is located in Maryville, Tennessee. DENSO Manufacturing Tennessee, Inc. is proud to be part of East Tennessee in one of the state's most historic counties. We are one of the region's largest employers.`	`2006 Honda Element Tensioner Pulley and Serpentine Belt - Duration: 19:37. Auto Parts Direct To You 62,653 views`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
num_train_epochs: 5
fp16: True
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: 128
per_device_eval_batch_size: 128
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 5
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
use_ipex: False
bf16: False
fp16: True
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}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	test-eval_cosine_accuracy
0.04	500	3.2317	-
0.08	1000	1.6755	0.9557
0.12	1500	1.3688	-
0.16	2000	1.236	0.9590
0.2	2500	1.1464	-
0.24	3000	1.1035	0.9611
0.28	3500	1.0254	-
0.32	4000	0.9914	0.9624
0.36	4500	0.9547	-
0.4	5000	0.9156	0.9642
0.44	5500	0.8922	-
0.48	6000	0.8565	0.9648
0.52	6500	0.8342	-
0.56	7000	0.8054	0.9667
0.6	7500	0.7731	-
0.64	8000	0.7567	0.9686
0.68	8500	0.7461	-
0.72	9000	0.7348	0.9699
0.76	9500	0.7155	-
0.8	10000	0.7016	0.9699
0.84	10500	0.6924	-
0.88	11000	0.6659	0.9711
0.92	11500	0.653	-
0.96	12000	0.6517	0.9717
1.0	12500	0.6402	0.9725
1.04	13000	0.5768	0.9721
1.08	13500	0.567	-
1.12	14000	0.5682	0.9731
1.16	14500	0.5554	-
1.2	15000	0.5513	0.9735
1.24	15500	0.55	-
1.28	16000	0.5339	0.9745
1.32	16500	0.532	-
1.3600	17000	0.5206	0.9749
1.4	17500	0.53	-
1.44	18000	0.5092	0.9754
1.48	18500	0.5096	-
1.52	19000	0.5061	0.9760
1.56	19500	0.5025	-
1.6	20000	0.4935	0.9764
1.6400	20500	0.4966	-
1.6800	21000	0.4816	0.9767
1.72	21500	0.4752	-
1.76	22000	0.473	0.9771
1.8	22500	0.4735	-
1.8400	23000	0.4548	0.9776
1.88	23500	0.4541	-
1.92	24000	0.4553	0.9780
1.96	24500	0.4469	-
2.0	25000	0.4508	0.9782
2.04	25500	0.3909	-
2.08	26000	0.3887	0.9784
2.12	26500	0.3885	-
2.16	27000	0.385	0.9783
2.2	27500	0.383	-
2.24	28000	0.3847	0.9791
2.2800	28500	0.3784	-
2.32	29000	0.3807	0.9791
2.36	29500	0.3749	-
2.4	30000	0.3746	0.9792
2.44	30500	0.3747	-
2.48	31000	0.3634	0.9796
2.52	31500	0.3711	-
2.56	32000	0.3733	0.9797
2.6	32500	0.3587	-
2.64	33000	0.3595	0.9797
2.68	33500	0.3609	-
2.7200	34000	0.3547	0.9802
2.76	34500	0.3606	-
2.8	35000	0.3503	0.9801
2.84	35500	0.356	-
2.88	36000	0.3431	0.9808
2.92	36500	0.3579	-
2.96	37000	0.352	0.9807
3.0	37500	0.3538	0.9807
3.04	38000	0.3072	0.9806
3.08	38500	0.3089	-
3.12	39000	0.3004	0.9810
3.16	39500	0.3066	-
3.2	40000	0.3184	0.9812
3.24	40500	0.3033	-
3.2800	41000	0.3055	0.9812
3.32	41500	0.2974	-
3.36	42000	0.3054	0.9814
3.4	42500	0.297	-
3.44	43000	0.2989	0.9816
3.48	43500	0.2982	-
3.52	44000	0.2911	0.9817
3.56	44500	0.2927	-
3.6	45000	0.3003	0.9819
3.64	45500	0.2953	-
3.68	46000	0.2951	0.9819
3.7200	46500	0.2875	-
3.76	47000	0.2947	0.9818
3.8	47500	0.2926	-
3.84	48000	0.2886	0.9821
3.88	48500	0.2902	-
3.92	49000	0.2881	0.9823
3.96	49500	0.293	-
4.0	50000	0.287	0.9823
4.04	50500	0.2668	-
4.08	51000	0.2612	0.9821
4.12	51500	0.2594	-
4.16	52000	0.2602	0.9824
4.2	52500	0.2623	-
4.24	53000	0.2593	0.9824
4.28	53500	0.2681	-
4.32	54000	0.2642	0.9826
4.36	54500	0.261	-
4.4	55000	0.2666	0.9825
4.44	55500	0.2628	-
4.48	56000	0.2598	0.9826
4.52	56500	0.2579	-
4.5600	57000	0.2622	0.9826
4.6	57500	0.2588	-
4.64	58000	0.2495	0.9827
4.68	58500	0.2642	-
4.72	59000	0.2558	0.9827
4.76	59500	0.2552	-
4.8	60000	0.2588	0.9827
4.84	60500	0.2658	-
4.88	61000	0.2577	0.9828

Framework Versions

Python: 3.11.5
Sentence Transformers: 4.1.0
Transformers: 4.42.4
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
Datasets: 3.5.0
Tokenizers: 0.19.1

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: 4 tokens mean: 9.07 tokens max: 35 tokens	min: 18 tokens mean: 78.28 tokens max: 187 tokens	min: 19 tokens mean: 76.02 tokens max: 220 tokens