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/FaLaBSE-v5")
# Run inference
sentences = [
    'آیا با دختری که باکره نیست ازدواج خواهید کرد؟',
    'آیا با کسی که باکره نیست ازدواج می کنید؟',
    'زنی با شلوار جین کنار اسبی با زین ایستاده است',
]
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: 149,098 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 15.1 tokens
max: 76 tokens
min: 5 tokens
mean: 14.54 tokens
max: 57 tokens

Samples:

anchor	positive
`اگر هند تقسیم نشده بود ، هند امروز چگونه به نظر می رسد؟`	`اگر پارتیشن اتفاق نیفتاد ، هند امروز چگونه خواهد بود؟`
`چگونه می توانم وارد امنیت اینترنت شوم؟`	`چگونه می توانم شروع به یادگیری امنیت اطلاعات کنم؟`
`برخی از بهترین مؤسسات مربیگری GMAT در دهلی/NCR چیست؟`	`بهترین مؤسسات مربیگری برای GMAT در NCR چیست؟`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
learning_rate: 2e-05
weight_decay: 0.01
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 32
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: 2e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
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
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: False
resume_from_checkpoint: None
hub_model_id: None
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: False
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

Epoch	Step	Training Loss
0.0429	100	0.0474
0.0858	200	0.0364
0.1288	300	0.0345
0.1717	400	0.0309
0.2146	500	0.0347
0.2575	600	0.0365
0.3004	700	0.0303
0.3433	800	0.0288
0.3863	900	0.029
0.4292	1000	0.0329
0.4721	1100	0.0351
0.5150	1200	0.0282
0.5579	1300	0.029
0.6009	1400	0.029
0.6438	1500	0.0278
0.6867	1600	0.028
0.7296	1700	0.0276
0.7725	1800	0.0306
0.8155	1900	0.0242
0.8584	2000	0.0254
0.9013	2100	0.0226
0.9442	2200	0.0261
0.9871	2300	0.0258
1.0300	2400	0.0245
1.0730	2500	0.0194
1.1159	2600	0.021
1.1588	2700	0.018
1.2017	2800	0.0201
1.2446	2900	0.0204
1.2876	3000	0.0178
1.3305	3100	0.0159
1.3734	3200	0.0184
1.4163	3300	0.0189
1.4592	3400	0.0194
1.5021	3500	0.0201
1.5451	3600	0.0164
1.5880	3700	0.0187
1.6309	3800	0.0181
1.6738	3900	0.0161
1.7167	4000	0.0195
1.7597	4100	0.0165
1.8026	4200	0.0175
1.8455	4300	0.016
1.8884	4400	0.0142
1.9313	4500	0.0187
1.9742	4600	0.0137
2.0172	4700	0.0173
2.0601	4800	0.015
2.1030	4900	0.0158
2.1459	5000	0.0135
2.1888	5100	0.0144
2.2318	5200	0.0135
2.2747	5300	0.0142
2.3176	5400	0.0129
2.3605	5500	0.0142
2.4034	5600	0.0141
2.4464	5700	0.0142
2.4893	5800	0.0141
2.5322	5900	0.0118
2.5751	6000	0.0142
2.6180	6100	0.0125
2.6609	6200	0.0107
2.7039	6300	0.0129
2.7468	6400	0.0114
2.7897	6500	0.0137
2.8326	6600	0.0108
2.8755	6700	0.0131
2.9185	6800	0.0114
2.9614	6900	0.0137

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	anchor	positive
type	string	string
details	min: 5 tokens mean: 15.1 tokens max: 76 tokens	min: 5 tokens mean: 14.54 tokens max: 57 tokens