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, 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
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'قرآن شراب اور جوئے کے بارے میں کیا کہتا ہے؟',
    'O you who have believed, indeed, intoxicants, gambling, [sacrificing on] stone alters [to other than Allah], and divining arrows are but defilement from the work of Satan, so avoid it that you may be successful.',
    'عَنِ الْخَيْرِ، وَكُنْتُ أَسْأَلُهُ عَنِ الشَّرِّ، مَخَافَةَ أَنْ يُدْرِكَنِي فَقُلْتُ يَا رَسُولَ اللَّهِ إِنَّا كُنَّا فِي جَاهِلِيَّةٍ وَشَرٍّ فَجَاءَنَا اللَّهُ بِهَذَا الْخَيْرِ، فَهَلْ بَعْدَ هَذَا الْخَيْرِ مِنْ شَرٍّ قَالَ \u200f"\u200f نَعَمْ \u200f"\u200f\u200f.\u200f قُلْتُ وَهَلْ بَعْدَ ذَلِكَ الشَّرِّ مِنْ خَيْرٍ قَالَ \u200f"\u200f نَعَمْ، وَفِيهِ دَخَنٌ \u200f"\u200f\u200f.\u200f قُلْتُ وَمَا دَخَنُهُ قَالَ \u200f"\u200f قَوْمٌ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6190, 0.1344],
#         [0.6190, 1.0000, 0.1736],
#         [0.1344, 0.1736, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 801 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 100 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
modality	text	text	text
details	min: 7 tokens mean: 13.95 tokens max: 24 tokens	min: 12 tokens mean: 107.9 tokens max: 512 tokens	min: 14 tokens mean: 179.42 tokens max: 512 tokens

Samples:

sentence_0	sentence_1	sentence_2
`هل يأمر القرآن بالعدل ولو على الأقربين؟`	`O you who have believed, be persistently standing firm in justice, witnesses for Allah, even if it be against yourselves or parents and relatives. Whether one is rich or poor, Allah is more worthy of both. So follow not [personal] inclination, lest you not be just. And if you distort [your testimony] or refuse [to give it], then indeed Allah is ever, with what you do, Acquainted.`	`رَسُولَ اللَّهِ صلى الله عليه وسلم قَالَ ‏ "‏ لاَ صَلاَةَ لِمَنْ لَمْ يَقْرَأْ بِأُمِّ الْقُرْآنِ ‏"‏ ‏.‏`
`متى تصلى صلاة الوتر؟`	`Ibn 'Umar reported the Messenger of Allah (ﷺ) as say- ing:Hasten to pray Witr before morning`	`سَقَتْ رَسُولَ اللَّهِ صلى الله عليه وسلم أَنْقَعَتْ لَهُ تَمَرَاتٍ مِنَ اللَّيْلِ، فَلَمَّا أَكَلَ سَقَتْهُ إِيَّاهُ‏.‏`
`ما هي الغيبة؟`	Abu Huraira reported Allah's Messenger (ﷺ) as saying:Do you know what is backbiting? They (the Companions) said: Allah and His Messenger know best. Thereupon he (the Holy Prophet) said: Backbiting implies your talking about your brother in a manner which he does not like. It was said to him: What is your opinion about this that if I actually find (that failing) in my brother which I made a mention of? He said: If (that failing) is actually found (in him) what you assert, you in fact backbited him, and if that is not in him it is a slander	`غَافِرِ ٱلذَّنۢبِ وَقَابِلِ ٱلتَّوْبِ شَدِيدِ ٱلْعِقَابِ ذِى ٱلطَّوْلِ ۖ لَآ إِلَٰهَ إِلَّا هُوَ ۖ إِلَيْهِ ٱلْمَصِيرُ`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Training Hyperparameters

Non-Default Hyperparameters

multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

per_device_train_batch_size: 8
num_train_epochs: 3
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1
label_smoothing_factor: 0.0
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 8
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Time

Training: 6.5 minutes

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.7.0
Transformers: 5.13.1
PyTorch: 2.11.0+cu128
Accelerate: 1.14.0
Datasets: 4.0.0
Tokenizers: 0.22.2

Additional Resources

Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}