This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. 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: intfloat/multilingual-e5-small
Maximum Sequence Length: 512 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': 512, '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})
  (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("codersan/eFuck")
# Run inference
sentences = [
    'برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟',
    'چگونه می توانم نویسنده برتر Quora شوم ، از صعود بیشتر و آمار بهتر استفاده کنم؟',
    'من به دنبال خرید دوچرخه جدید هستم.Suzuki Gixxer 155 یا Honda Hornet 160r.کدام یک را بخرید؟',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 131,157 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 6 tokens
mean: 16.81 tokens
max: 97 tokens
min: 5 tokens
mean: 16.59 tokens
max: 59 tokens

Samples:

anchor	positive
`وقتی سوال من به عنوان "این سوال ممکن است به ویرایش نیاز داشته باشد" چه کاری باید انجام دهم ، اما نمی توانم دلیل آن را پیدا کنم؟`	`چرا سوال من به عنوان نیاز به پیشرفت مشخص شده است؟`
`چگونه می توانید یک فایل رمزگذاری شده را با دانستن اینکه این یک فایل تصویری است بدون دانستن گسترش پرونده یا کلید ، رمزگشایی کنید؟`	`چگونه می توانید یک فایل رمزگذاری شده را رمزگشایی کنید و بدانید که این یک فایل تصویری است بدون اینکه از پسوند پرونده اطلاع داشته باشید؟`
`احساس می کنم خودکشی می کنم ، چگونه باید با آن برخورد کنم؟`	`احساس می کنم خودکشی می کنم.چه کاری باید انجام دهم؟`

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: 0.005
num_train_epochs: 10
warmup_ratio: 0.1
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: 0.005
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 10
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: 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

Click to expand

Epoch	Step	Training Loss
0.0244	100	1.3984
0.0488	200	0.8762
0.0732	300	0.2492
0.0976	400	0.0754
0.1220	500	0.0809
0.1464	600	0.0789
0.1708	700	0.076
0.1952	800	0.0642
0.2196	900	0.0743
0.2440	1000	0.0605
0.2684	1100	0.0705
0.2928	1200	0.0594
0.3172	1300	0.0565
0.3415	1400	0.071
0.3659	1500	0.0476
0.3903	1600	0.0514
0.4147	1700	0.0584
0.4391	1800	0.0649
0.4635	1900	0.0485
0.4879	2000	0.0556
0.5123	2100	0.0594
0.5367	2200	0.0556
0.5611	2300	0.0439
0.5855	2400	0.0619
0.6099	2500	0.0553
0.6343	2600	0.0393
0.6587	2700	0.0458
0.6831	2800	0.0476
0.7075	2900	0.0535
0.7319	3000	0.0439
0.7563	3100	0.0438
0.7807	3200	0.052
0.8051	3300	0.0514
0.8295	3400	0.0549
0.8539	3500	0.0439
0.8783	3600	0.0429
0.9027	3700	0.0442
0.9271	3800	0.0643
0.9515	3900	0.0408
0.9758	4000	0.0403
1.0002	4100	0.0446
1.0246	4200	0.0527
1.0490	4300	0.0545
1.0734	4400	0.0517
1.0978	4500	0.0299
1.1222	4600	0.0444
1.1466	4700	0.0475
1.1710	4800	0.0414
1.1954	4900	0.0386
1.2198	5000	0.0508
1.2442	5100	0.0384
1.2686	5200	0.0453
1.2930	5300	0.0401
1.3174	5400	0.0328
1.3418	5500	0.0456
1.3662	5600	0.0295
1.3906	5700	0.0366
1.4150	5800	0.0431
1.4394	5900	0.0442
1.4638	6000	0.0343
1.4882	6100	0.0405
1.5126	6200	0.0357
1.5370	6300	0.0423
1.5614	6400	0.0288
1.5858	6500	0.0393
1.6101	6600	0.0369
1.6345	6700	0.0245
1.6589	6800	0.0286
1.6833	6900	0.0325
1.7077	7000	0.0311
1.7321	7100	0.0272
1.7565	7200	0.0261
1.7809	7300	0.0296
1.8053	7400	0.0343
1.8297	7500	0.036
1.8541	7600	0.0225
1.8785	7700	0.0232
1.9029	7800	0.0275
1.9273	7900	0.0394
1.9517	8000	0.0297
1.9761	8100	0.0249
2.0005	8200	0.0268
2.0249	8300	0.0269
2.0493	8400	0.0296
2.0737	8500	0.0326
2.0981	8600	0.0183
2.1225	8700	0.024
2.1469	8800	0.0298
2.1713	8900	0.0273
2.1957	9000	0.0244
2.2201	9100	0.0308
2.2444	9200	0.0247
2.2688	9300	0.0299
2.2932	9400	0.0222
2.3176	9500	0.0213
2.3420	9600	0.0316
2.3664	9700	0.0157
2.3908	9800	0.0248
2.4152	9900	0.028
2.4396	10000	0.0269
2.4640	10100	0.0214
2.4884	10200	0.0242
2.5128	10300	0.0222
2.5372	10400	0.0253
2.5616	10500	0.0175
2.5860	10600	0.0269
2.6104	10700	0.0281
2.6348	10800	0.014
2.6592	10900	0.0187
2.6836	11000	0.0204
2.7080	11100	0.0228
2.7324	11200	0.0193
2.7568	11300	0.014
2.7812	11400	0.0171
2.8056	11500	0.0213
2.8300	11600	0.025
2.8544	11700	0.0138
2.8788	11800	0.0133
2.9031	11900	0.021
2.9275	12000	0.0256
2.9519	12100	0.019
2.9763	12200	0.0149
3.0007	12300	0.0192
3.0251	12400	0.0194
3.0495	12500	0.0179
3.0739	12600	0.0218
3.0983	12700	0.0126
3.1227	12800	0.018
3.1471	12900	0.0188
3.1715	13000	0.0181
3.1959	13100	0.0186
3.2203	13200	0.0235
3.2447	13300	0.0172
3.2691	13400	0.0183
3.2935	13500	0.0155
3.3179	13600	0.0135
3.3423	13700	0.0236
3.3667	13800	0.0115
3.3911	13900	0.0162
3.4155	14000	0.0207
3.4399	14100	0.0174
3.4643	14200	0.0128
3.4887	14300	0.0202
3.5131	14400	0.0165
3.5374	14500	0.0162
3.5618	14600	0.015
3.5862	14700	0.0203
3.6106	14800	0.0222
3.6350	14900	0.0105
3.6594	15000	0.014
3.6838	15100	0.0146
3.7082	15200	0.015
3.7326	15300	0.0153
3.7570	15400	0.0099
3.7814	15500	0.0105
3.8058	15600	0.0168
3.8302	15700	0.0185
3.8546	15800	0.0104
3.8790	15900	0.01
3.9034	16000	0.0142
3.9278	16100	0.0197
3.9522	16200	0.013
3.9766	16300	0.0137
4.0010	16400	0.0133
4.0254	16500	0.0132
4.0498	16600	0.0124
4.0742	16700	0.0141
4.0986	16800	0.0099
4.1230	16900	0.0113
4.1474	17000	0.0149
4.1717	17100	0.0145
4.1961	17200	0.0129
4.2205	17300	0.0185
4.2449	17400	0.0138
4.2693	17500	0.0133
4.2937	17600	0.0107
4.3181	17700	0.0092
4.3425	17800	0.0175
4.3669	17900	0.0097
4.3913	18000	0.0111
4.4157	18100	0.0136
4.4401	18200	0.0122
4.4645	18300	0.0095
4.4889	18400	0.0141
4.5133	18500	0.0094
4.5377	18600	0.0123
4.5621	18700	0.0108
4.5865	18800	0.0145
4.6109	18900	0.0195
4.6353	19000	0.0099
4.6597	19100	0.0107
4.6841	19200	0.0105
4.7085	19300	0.0124
4.7329	19400	0.012
4.7573	19500	0.0081
4.7817	19600	0.0081
4.8061	19700	0.0111
4.8304	19800	0.0141
4.8548	19900	0.0073
4.8792	20000	0.0094
4.9036	20100	0.011
4.9280	20200	0.0157
4.9524	20300	0.0086
4.9768	20400	0.0093
5.0012	20500	0.011
5.0256	20600	0.0107
5.0500	20700	0.0094
5.0744	20800	0.008
5.0988	20900	0.0076
5.1232	21000	0.0088
5.1476	21100	0.0119
5.1720	21200	0.0118
5.1964	21300	0.0105
5.2208	21400	0.0138
5.2452	21500	0.0109
5.2696	21600	0.0101
5.2940	21700	0.008
5.3184	21800	0.0068
5.3428	21900	0.0123
5.3672	22000	0.0086
5.3916	22100	0.0084
5.4160	22200	0.0113
5.4404	22300	0.0086
5.4647	22400	0.0076
5.4891	22500	0.0101
5.5135	22600	0.0083
5.5379	22700	0.0116
5.5623	22800	0.0083
5.5867	22900	0.0137
5.6111	23000	0.0144
5.6355	23100	0.0081
5.6599	23200	0.006
5.6843	23300	0.0096
5.7087	23400	0.0098
5.7331	23500	0.0096
5.7575	23600	0.0063
5.7819	23700	0.0052
5.8063	23800	0.008
5.8307	23900	0.0117
5.8551	24000	0.0053
5.8795	24100	0.0077
5.9039	24200	0.0086
5.9283	24300	0.0129
5.9527	24400	0.0085
5.9771	24500	0.0064
6.0015	24600	0.0092
6.0259	24700	0.0076
6.0503	24800	0.0078
6.0747	24900	0.0074
6.0990	25000	0.0064
6.1234	25100	0.0067
6.1478	25200	0.0091
6.1722	25300	0.0087
6.1966	25400	0.0076
6.2210	25500	0.0104
6.2454	25600	0.0077
6.2698	25700	0.0074
6.2942	25800	0.0055
6.3186	25900	0.0059
6.3430	26000	0.0092
6.3674	26100	0.0051
6.3918	26200	0.0075
6.4162	26300	0.0093
6.4406	26400	0.0073
6.4650	26500	0.0051
6.4894	26600	0.0093
6.5138	26700	0.0065
6.5382	26800	0.0072
6.5626	26900	0.0075
6.5870	27000	0.0111
6.6114	27100	0.0139
6.6358	27200	0.0066
6.6602	27300	0.0062
6.6846	27400	0.0078
6.7090	27500	0.0084
6.7333	27600	0.0077
6.7577	27700	0.0055
6.7821	27800	0.0039
6.8065	27900	0.0082
6.8309	28000	0.0101
6.8553	28100	0.0041
6.8797	28200	0.0058
6.9041	28300	0.0058
6.9285	28400	0.0109
6.9529	28500	0.0054
6.9773	28600	0.0061
7.0017	28700	0.0078
7.0261	28800	0.0065
7.0505	28900	0.0061
7.0749	29000	0.0049
7.0993	29100	0.0062
7.1237	29200	0.0052
7.1481	29300	0.0073
7.1725	29400	0.0072
7.1969	29500	0.0067
7.2213	29600	0.0093
7.2457	29700	0.008
7.2701	29800	0.0057
7.2945	29900	0.0051
7.3189	30000	0.0046
7.3433	30100	0.0078
7.3677	30200	0.0041
7.3920	30300	0.0054
7.4164	30400	0.008
7.4408	30500	0.0056
7.4652	30600	0.0037
7.4896	30700	0.0071
7.5140	30800	0.0058
7.5384	30900	0.0074
7.5628	31000	0.0059
7.5872	31100	0.0088
7.6116	31200	0.0102
7.6360	31300	0.0058
7.6604	31400	0.0044
7.6848	31500	0.0065
7.7092	31600	0.007
7.7336	31700	0.0078
7.7580	31800	0.0048
7.7824	31900	0.0033
7.8068	32000	0.0063
7.8312	32100	0.008
7.8556	32200	0.004
7.8800	32300	0.0057
7.9044	32400	0.005
7.9288	32500	0.0095
7.9532	32600	0.0042
7.9776	32700	0.0058
8.0020	32800	0.006
8.0263	32900	0.006
8.0507	33000	0.0054
8.0751	33100	0.0041
8.0995	33200	0.0045
8.1239	33300	0.0052
8.1483	33400	0.0067
8.1727	33500	0.008
8.1971	33600	0.0047
8.2215	33700	0.0079
8.2459	33800	0.0071
8.2703	33900	0.0043
8.2947	34000	0.0041
8.3191	34100	0.0035
8.3435	34200	0.0059
8.3679	34300	0.004
8.3923	34400	0.005
8.4167	34500	0.0067
8.4411	34600	0.0049
8.4655	34700	0.0034
8.4899	34800	0.0057
8.5143	34900	0.0052
8.5387	35000	0.005
8.5631	35100	0.0047
8.5875	35200	0.0089
8.6119	35300	0.0066
8.6363	35400	0.0044
8.6606	35500	0.0037
8.6850	35600	0.0059
8.7094	35700	0.0069
8.7338	35800	0.0069
8.7582	35900	0.0038
8.7826	36000	0.0028
8.8070	36100	0.0047
8.8314	36200	0.007
8.8558	36300	0.0036
8.8802	36400	0.0049
8.9046	36500	0.0041
8.9290	36600	0.0085
8.9534	36700	0.004
8.9778	36800	0.0044
9.0022	36900	0.0053
9.0266	37000	0.006
9.0510	37100	0.0051
9.0754	37200	0.0029
9.0998	37300	0.0041
9.1242	37400	0.0046
9.1486	37500	0.0057
9.1730	37600	0.0063
9.1974	37700	0.0048
9.2218	37800	0.0077
9.2462	37900	0.0056
9.2706	38000	0.0039
9.2949	38100	0.0036
9.3193	38200	0.0032
9.3437	38300	0.0055
9.3681	38400	0.0037
9.3925	38500	0.0045
9.4169	38600	0.0065
9.4413	38700	0.0047
9.4657	38800	0.0033
9.4901	38900	0.0052
9.5145	39000	0.0043
9.5389	39100	0.0043
9.5633	39200	0.0049
9.5877	39300	0.0074
9.6121	39400	0.0054
9.6365	39500	0.004
9.6609	39600	0.0031
9.6853	39700	0.0054
9.7097	39800	0.0061
9.7341	39900	0.0055
9.7585	40000	0.0033
9.7829	40100	0.0028
9.8073	40200	0.0046
9.8317	40300	0.0062
9.8561	40400	0.0033
9.8805	40500	0.0047
9.9049	40600	0.0045
9.9293	40700	0.0075
9.9536	40800	0.0035
9.9780	40900	0.0038

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: 4.0.0
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}
}

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details	min: 6 tokens mean: 16.81 tokens max: 97 tokens	min: 5 tokens mean: 16.59 tokens max: 59 tokens