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-v4")
# Run inference
sentences = [
    'معنی و هدف زندگی چیست؟',
    'معنی دقیق زندگی چیست؟',
    'چه فیلم هایی را به همه توصیه می کنید که تماشا کنند؟',
]
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: 165,665 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 14.65 tokens
max: 48 tokens
min: 5 tokens
mean: 14.87 tokens
max: 53 tokens

Samples:

anchor	positive
`طالع بینی: من یک ماه و کلاه درپوش خورشید است ... این در مورد من چه می گوید؟`	`من یک برج سه گانه (خورشید ، ماه و صعود در برجستگی) هستم که این در مورد من چه می گوید؟`
`چگونه می توانم یک زمین شناس خوب باشم؟`	`چه کاری باید انجام دهم تا یک زمین شناس عالی باشم؟`
`چگونه می توانم نظرات YouTube خود را بخوانم و پیدا کنم؟`	`چگونه می توانم تمام نظرات YouTube خود را ببینم؟`

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
0.0386	100	0.0863
0.0772	200	0.0652
0.1159	300	0.0595
0.1545	400	0.0614
0.1931	500	0.05
0.2317	600	0.0453
0.2704	700	0.0579
0.3090	800	0.0542
0.3476	900	0.0534
0.3862	1000	0.0532
0.4249	1100	0.0548
0.4635	1200	0.0519
0.5021	1300	0.0547
0.5407	1400	0.0563
0.5794	1500	0.0474
0.6180	1600	0.0433
0.6566	1700	0.0545
0.6952	1800	0.0509
0.7339	1900	0.0453
0.7725	2000	0.0446
0.8111	2100	0.0506
0.8497	2200	0.046
0.8884	2300	0.0413
0.9270	2400	0.149
0.9656	2500	0.6993
1.0039	2600	1.081
1.0425	2700	0.0397
1.0811	2800	0.0337
1.1197	2900	0.0307
1.1584	3000	0.0323
1.1970	3100	0.0273
1.2356	3200	0.0292
1.2742	3300	0.0323
1.3129	3400	0.0352
1.3515	3500	0.0281
1.3901	3600	0.0318
1.4287	3700	0.0281
1.4674	3800	0.0304
1.5060	3900	0.0321
1.5446	4000	0.035
1.5832	4100	0.0279
1.6219	4200	0.0286
1.6605	4300	0.0333
1.6991	4400	0.0323
1.7377	4500	0.0312
1.7764	4600	0.0261
1.8150	4700	0.0361
1.8536	4800	0.0306
1.8922	4900	0.028
1.9309	5000	0.1226
1.9695	5100	0.5625
2.0077	5200	0.8337
2.0463	5300	0.0273
2.0850	5400	0.0242
2.1236	5500	0.0236
2.1622	5600	0.0237
2.2008	5700	0.0197
2.2395	5800	0.0217
2.2781	5900	0.0244
2.3167	6000	0.027
2.3553	6100	0.0235
2.3940	6200	0.0233
2.4326	6300	0.0225
2.4712	6400	0.023
2.5098	6500	0.023
2.5485	6600	0.0243
2.5871	6700	0.0215
2.6257	6800	0.0236
2.6643	6900	0.0234
2.7030	7000	0.0239
2.7416	7100	0.0248
2.7802	7200	0.02
2.8188	7300	0.0271
2.8575	7400	0.0235
2.8961	7500	0.0214
2.9347	7600	0.1147
2.9733	7700	0.5838

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: 14.65 tokens max: 48 tokens	min: 5 tokens mean: 14.87 tokens max: 53 tokens