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vishal1210/sanskrit-english-embedding

sourceHugging Faceupdated 1mo agoView on Hugging Face

1likes63downloads

Model Card

SentenceTransformer based on intfloat/multilingual-e5-small

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. It maps inputs to a 384-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-small
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 384 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': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'query: All these are knowers of sacrifice, whose sins are destroyed by sacrifice.',
    'passage: अपरे नियताहाराः प्राणान्प्राणेषु जुह्वति |सर्वेऽप्येते यज्ञविदो यज्ञक्षपितकल्मषाः |',
    'passage: Arjuna, I know all beings, past as well as present, nay, even those that are yet to come; but noneknows Me.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5620,  0.0049],
#         [ 0.5620,  1.0000, -0.0577],
#         [ 0.0049, -0.0577,  1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 1,120 training samples
—Columns: <code>sentence0</code> and <code>sentence1</code>
—Approximate statistics based on the first 100 samples: | | sentence0 | sentence1 | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | <ul><li>min: 31 tokens</li><li>mean: 44.72 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 44.09 tokens</li><li>max: 95 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>query: Indeed all these are noble, but the man of wisdom is My very self: such is My view. For such a devotee, who has his mind and intellect merged in Me, is firmly established in Me alone, the highest goal.</code> | <code>passage: उदाराः सर्व एवैते ज्ञानी त्वात्मैव मे मतम् \|आस्थितः स हि युक्तात्मा मामेवानुत्तमां गतिम् \|</code> | | <code>query: श्रीभगवानुवाच \|मया प्रसन्नेन तवार्जुनेदंरूपं परं दर्शितमात्मयोगात् \|तेजोमयं विश्वमनन्तमाद्यंयन्मे त्वदन्येन न दृष्टपूर्वम् \|</code> | <code>passage: Shree Bhagavan said: Arjuna! pleased with you I have shown you, through My own power of Yoga, this supreme, effulgent, primal and infinite Cosmic Body, which was never seen before by any else than you.</code> | | <code>query: Having transcended the aforesaid three Gunas, which have caused the body, and freed from birth, death, old age and all kinds of sorrow, this soul attains supreme bliss.</code> | <code>passage: गुणानेतानतीत्य त्रीन्देही देहसमुद्भवान् \|जन्ममृत्युजरादुःखैर्विमुक्तोऽमृतमश्नुते \|</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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

—per_device_train_batch_size: 16
—num_train_epochs: 1
—per_device_eval_batch_size: 16
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

—per_device_train_batch_size: 16
—num_train_epochs: 1
—max_steps: -1
—learning_rate: 5e-05
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_steps: 0
—optim: adamwtorchfused
—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: 16
—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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—dataloader_drop_last: False
—dataloader_num_workers: 0
—dataloader_pin_memory: True
—dataloader_persistent_workers: False
—dataloader_prefetch_factor: None
—dataloader_multiprocessing_context: None
—dataloader_in_order: True
—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
—local_rank: -1
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}
—warmup_ratio: None

</details>

Training Time

—Training: 25.5 seconds

Framework Versions

—Python: 3.13.15
—Sentence Transformers: 6.0.0
—Transformers: 5.15.1
—PyTorch: 2.11.0+cu128
—Accelerate: 1.14.0
—Datasets: 5.0.1
—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

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{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},
}

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