SentenceTransformer based on intfloat/multilingual-e5-base

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base. 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: intfloat/multilingual-e5-base
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("codersan/newfa_e5base")
# Run inference
sentences = [
    'من می خواهم آماده سازی برای امتحان IAS را شروع کنم ، چگونه باید ادامه دهم؟',
    'چگونه می توانم آماده سازی برای آزمون UPSC را شروع کنم؟',
    'یک کوهنورد یک صخره را می\u200cگیرد و مرد دیگر یک دیوار را با طناب می\u200cبندد',
]
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: 126,423 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 17.43 tokens
max: 109 tokens

min: 6 tokens
mean: 16.34 tokens
max: 59 tokens

	anchor	positive
type	string	string
details	min: 5 tokens mean: 17.43 tokens max: 109 tokens	min: 6 tokens mean: 16.34 tokens max: 59 tokens

Samples:

anchor	positive
`خانواده در حال تماشای یک پسر کوچک است که به توپ بیسبال ضربه می‌زند`	`خانواده در حال تماشای پسری است که به توپ بیسبال ضربه می‌زند`
`چرا هند باید محصولات چین را خریداری کند اگر آنها محصولات ما را خریداری نکنند؟ و بیشتر از آن در برابر هند است از هر جنبه ای. آیا ما محصولات چینی را تحریم می کنیم؟`	`اگر چین خیلی مخالف هند است ، چرا هندی ها از خرید محصولات چینی دست نمی کشند؟`
`چه تفاوتی بین همه جانبه و قادر مطلق وجود دارد؟`	`تفاوت های بین همه چیز و قادر مطلق چیست؟`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 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: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
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
0.0253	100	0.1051
0.0506	200	0.0588
0.0759	300	0.0628
0.1012	400	0.0388
0.1266	500	0.0464
0.1519	600	0.0437
0.1772	700	0.0456
0.2025	800	0.0411
0.2278	900	0.0425
0.2531	1000	0.0472
0.2784	1100	0.05
0.3037	1200	0.0381
0.3290	1300	0.0458
0.3543	1400	0.0387
0.3797	1500	0.0472
0.4050	1600	0.052
0.4303	1700	0.0432
0.4556	1800	0.0415
0.4809	1900	0.0311
0.5062	2000	0.0375
0.5315	2100	0.0436
0.5568	2200	0.0392
0.5821	2300	0.0338
0.6074	2400	0.033
0.6328	2500	0.0389
0.6581	2600	0.032
0.6834	2700	0.0355
0.7087	2800	0.0378
0.7340	2900	0.0372
0.7593	3000	0.0426
0.7846	3100	0.0396
0.8099	3200	0.0382
0.8352	3300	0.0368
0.8605	3400	0.0446
0.8859	3500	0.0342
0.9112	3600	0.0367
0.9365	3700	0.0343
0.9618	3800	0.0408
0.9871	3900	0.0315

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

@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

@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}
}

codersan
/

newfa_e5base