This is a sentence-transformers model finetuned from sentence-transformers/LaBSE. It maps sentences & paragraphs to a 768-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/LaBSE
Maximum Sequence Length: 256 tokens
Output Dimensionality: 768 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': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): 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("codersan/validadted_faLabse_withCosSim")
# Run inference
sentences = [
    'داشتن هزاران دنبال کننده در Quora چگونه است؟',
    'چه چیزی است که ده ها هزار دنبال کننده در Quora داشته باشید؟',
    'چگونه Airprint HP OfficeJet 4620 با HP LaserJet Enterprise M606X مقایسه می شود؟',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 172,826 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 15.2 tokens
max: 80 tokens
min: 5 tokens
mean: 15.47 tokens
max: 50 tokens
min: 0.76
mean: 0.95
max: 1.0

Samples:

sentence1	sentence2	score
`تفاوت بین تحلیلگر تحقیقات بازار و تحلیلگر تجارت چیست؟`	`تفاوت بین تحقیقات بازاریابی و تحلیلگر تجارت چیست؟`	`0.982593297958374`
`خوردن چه چیزی باعث دل درد میشود؟`	`چه چیزی باعث رفع دل درد میشود؟`	`0.9582258462905884`
`بهترین نرم افزار ویرایش ویدیویی کدام است؟`	`بهترین نرم افزار برای ویرایش ویدیو چیست؟`	`0.9890836477279663`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 12
learning_rate: 5e-06
weight_decay: 0.01
num_train_epochs: 1
warmup_ratio: 0.1
push_to_hub: True
hub_model_id: codersan/validadted_faLabse_withCosSim
eval_on_start: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 12
per_device_eval_batch_size: 8
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-06
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 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}
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: True
resume_from_checkpoint: None
hub_model_id: codersan/validadted_faLabse_withCosSim
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
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
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: True
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0	0	-
0.0069	100	0.0299
0.0139	200	0.0185
0.0208	300	0.0063
0.0278	400	0.0021
0.0347	500	0.0009
0.0417	600	0.0006
0.0486	700	0.0006
0.0555	800	0.0005
0.0625	900	0.0005
0.0694	1000	0.0005
0.0764	1100	0.0005
0.0833	1200	0.0004
0.0903	1300	0.0004
0.0972	1400	0.0004
0.1041	1500	0.0004
0.1111	1600	0.0004
0.1180	1700	0.0004
0.1250	1800	0.0003
0.1319	1900	0.0003
0.1389	2000	0.0003
0.1458	2100	0.0003
0.1527	2200	0.0003
0.1597	2300	0.0003
0.1666	2400	0.0003
0.1736	2500	0.0003
0.1805	2600	0.0003
0.1875	2700	0.0003
0.1944	2800	0.0003
0.2013	2900	0.0003
0.2083	3000	0.0003
0.2152	3100	0.0003
0.2222	3200	0.0002
0.2291	3300	0.0003
0.2361	3400	0.0003
0.2430	3500	0.0002
0.2499	3600	0.0003
0.2569	3700	0.0003
0.2638	3800	0.0003
0.2708	3900	0.0002
0.2777	4000	0.0003
0.2847	4100	0.0003
0.2916	4200	0.0002
0.2985	4300	0.0002
0.3055	4400	0.0002
0.3124	4500	0.0002
0.3194	4600	0.0002
0.3263	4700	0.0002
0.3333	4800	0.0003
0.3402	4900	0.0002
0.3471	5000	0.0002
0.3541	5100	0.0002
0.3610	5200	0.0002
0.3680	5300	0.0002
0.3749	5400	0.0002
0.3819	5500	0.0002
0.3888	5600	0.0002
0.3958	5700	0.0002
0.4027	5800	0.0002
0.4096	5900	0.0002
0.4166	6000	0.0002
0.4235	6100	0.0002
0.4305	6200	0.0002
0.4374	6300	0.0002
0.4444	6400	0.0002
0.4513	6500	0.0002
0.4582	6600	0.0002
0.4652	6700	0.0002
0.4721	6800	0.0002
0.4791	6900	0.0002
0.4860	7000	0.0002
0.4930	7100	0.0002
0.4999	7200	0.0002
0.5068	7300	0.0002
0.5138	7400	0.0002
0.5207	7500	0.0002
0.5277	7600	0.0002
0.5346	7700	0.0002
0.5416	7800	0.0002
0.5485	7900	0.0002
0.5554	8000	0.0002
0.5624	8100	0.0002
0.5693	8200	0.0002
0.5763	8300	0.0002
0.5832	8400	0.0002
0.5902	8500	0.0002
0.5971	8600	0.0002
0.6040	8700	0.0002
0.6110	8800	0.0002
0.6179	8900	0.0002
0.6249	9000	0.0002
0.6318	9100	0.0002
0.6388	9200	0.0002
0.6457	9300	0.0002
0.6526	9400	0.0002
0.6596	9500	0.0002
0.6665	9600	0.0002
0.6735	9700	0.0002
0.6804	9800	0.0002
0.6874	9900	0.0002
0.6943	10000	0.0002
0.7012	10100	0.0002
0.7082	10200	0.0002
0.7151	10300	0.0002
0.7221	10400	0.0002
0.7290	10500	0.0002
0.7360	10600	0.0002
0.7429	10700	0.0002
0.7498	10800	0.0002
0.7568	10900	0.0002
0.7637	11000	0.0002
0.7707	11100	0.0002
0.7776	11200	0.0002
0.7846	11300	0.0002
0.7915	11400	0.0002
0.7984	11500	0.0002
0.8054	11600	0.0002
0.8123	11700	0.0002
0.8193	11800	0.0002
0.8262	11900	0.0002
0.8332	12000	0.0002
0.8401	12100	0.0002
0.8470	12200	0.0002
0.8540	12300	0.0002
0.8609	12400	0.0002
0.8679	12500	0.0002
0.8748	12600	0.0002
0.8818	12700	0.0002
0.8887	12800	0.0002
0.8956	12900	0.0002
0.9026	13000	0.0002
0.9095	13100	0.0002
0.9165	13200	0.0002
0.9234	13300	0.0002
0.9304	13400	0.0002
0.9373	13500	0.0002
0.9442	13600	0.0002
0.9512	13700	0.0002
0.9581	13800	0.0002
0.9651	13900	0.0002
0.9720	14000	0.0002
0.9790	14100	0.0002
0.9859	14200	0.0002
0.9928	14300	0.0002
0.9998	14400	0.0002

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.0
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

	sentence1	sentence2	score
type	string	string	float
details	min: 5 tokens mean: 15.2 tokens max: 80 tokens	min: 5 tokens mean: 15.47 tokens max: 50 tokens	min: 0.76 mean: 0.95 max: 1.0