| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:4858 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sanganaka/bge-m3-sanskritFT |
| widget: |
| - source_sentence: I've achieved a lot in my career, but I still feel a deep sense |
| of emptiness. I thought reaching these milestones would bring lasting satisfaction, |
| but it hasn't. Was it all for nothing? What is my true purpose if external achievements |
| don't fulfill me? |
| sentences: |
| - abhyāsa-yoga-yuktena cetasā nānya-gāminā | paramaṃ puruṣaṃ divyaṃ yāti pārthānucintayan |
| ||8|| |
| - abhyāse 'py asamartho 'si mat-karma-paramo bhava | mad-artham api karmāṇi kurvan |
| siddhim avāpsyasi ||10|| |
| - na kartṛtvaṃ na karmāṇi lokasya sṛjati prabhuḥ | na karma-phala-saṃyogaṃ svabhāvas |
| tu pravartate ||14|| |
| - source_sentence: I always feel so tired and sluggish, even after a full night's |
| sleep. My mind feels foggy, and I can't concentrate at work. What can I do to |
| regain my vitality and focus? |
| sentences: |
| - ye tu dharmyāmṛtam idaṃ yathoktaṃ paryupāsate | śraddadhānā mat-paramā bhaktās |
| te 'tīva me priyāḥ ||20|| |
| - āyuḥ-sattva-balārogya-sukha-prīti-vivardhanāḥ | rasyāḥ snigdhāḥ sthirā hṛdyā āhārāḥ |
| sāttvika-priyāḥ ||8|| |
| - devān bhāvayatānena te devā bhāvayantu vaḥ | parasparaṃ bhāvayantaḥ śreyaḥ param |
| avāpsyatha ||11|| |
| - source_sentence: I'm a working parent, constantly juggling responsibilities, and |
| I feel utterly overwhelmed and burnt out. I don't have a moment for myself, and |
| I'm losing my sense of self. |
| sentences: |
| - idaṃ jñānam upāśritya mama sādharmyam āgatāḥ | sarge 'pi nopajāyante pralaye na |
| vyathanti ca ||2|| |
| - teṣām evānukampārtham aham ajñānajaṃ tamaḥ | nāśayāmy ātma-bhāva-stho jñāna-dīpena |
| bhāsvatā ||11|| |
| - amānitvam adambhitvam ahiṃsā kṣāntir ārjavam | ācāryopāsanaṃ śaucaṃ sthairyam |
| ātma-vinigrahaḥ ||7|| indriyārtheṣu vairāgyam anahaṃkāra eva ca | janma-mṛtyu-jarā-vyādhi-duḥkha-doṣānudarśanam |
| ||8|| asaktir anabhiṣvaṅgaḥ putra-dāra-gṛhādiṣu | nityaṃ ca sama-cittatvam iṣṭāniṣṭopapattiṣu |
| ||9|| mayi cānanya-yogena bhaktir avyabhicāriṇī | vivikta-deśa-sevitvam aratir |
| jana-saṃsadi ||10|| adhyātma-jñāna-nityatvaṃ tattva-jñānārtha-darśanam | etaj |
| jñānam iti proktam ajñānaṃ yad ato 'nyathā ||11|| |
| - source_sentence: I've always been so worried about what others think of me, especially |
| online. One negative comment can ruin my entire day, even if there are hundreds |
| of positive ones. How can I develop a stronger sense of self-worth that isn't |
| dependent on external validation? |
| sentences: |
| - nirmāna-mohā jita-saṅga-doṣā adhyātma-nityā vinivṛtta-kāmāḥ | dvandvair vimuktāḥ |
| sukha-duḥkha-saṃjñair gacchanty amūḍhāḥ padam avyayaṃ tat ||5|| |
| - pravṛttiṃ ca nivṛttiṃ ca janā na vidur āsurāḥ | na śaucaṃ nāpi cācāro na satyaṃ |
| teṣu vidyate ||7|| |
| - samaḥ śatrau ca mitre ca tathā mānāpamānayoḥ | śītoṣṇa-sukha-duḥkheṣu samaḥ saṅga-vivarjitaḥ |
| ||18|| tulya-nindā-stutir maunī saṃtuṣṭo yena kenacit | aniketaḥ sthira-matir |
| bhaktimān me priyo naraḥ ||19|| |
| - source_sentence: I've been grieving a significant loss for a long time, and while |
| I know I need to move forward, my thoughts constantly pull me back to the past. |
| How do I let go and find peace? |
| sentences: |
| - daivī saṃpad vimokṣāya nibandhāyāsurī matā | mā śucaḥ saṃpadaṃ daivīm abhijāto |
| 'si pāṇḍava ||5|| |
| - etair vimuktaḥ kaunteya tamo-dvārais tribhir naraḥ | ācaraty ātmanaḥ śreyas tato |
| yāti parāṃ gatim ||22|| |
| - uddhared ātmanātmānaṃ nātmānam avasādayet | ātmaiva hy ātmano bandhur ātmaiva |
| ripur ātmanaḥ ||5|| |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on sanganaka/bge-m3-sanskritFT |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sanganaka/bge-m3-sanskritFT](https://huggingface.co/sanganaka/bge-m3-sanskritFT). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sanganaka/bge-m3-sanskritFT](https://huggingface.co/sanganaka/bge-m3-sanskritFT) <!-- at revision bcad4d3ffe0990d09bbc07f821bbbd5050ba0530 --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### 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': 1024, 'pooling_mode': 'cls', 'include_prompt': True}) |
| (2): Normalize({}) |
| ) |
| ``` |
|
|
| ## 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 = [ |
| "I've been grieving a significant loss for a long time, and while I know I need to move forward, my thoughts constantly pull me back to the past. How do I let go and find peace?", |
| 'uddhared ātmanātmānaṃ nātmānam avasādayet | ātmaiva hy ātmano bandhur ātmaiva ripur ātmanaḥ ||5||', |
| 'etair vimuktaḥ kaunteya tamo-dvārais tribhir naraḥ | ācaraty ātmanaḥ śreyas tato yāti parāṃ gatim ||22||', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 1024] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.4964, 0.1087], |
| # [0.4964, 1.0000, 0.3406], |
| # [0.1087, 0.3406, 1.0000]]) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 4,858 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code> |
| * Approximate statistics based on the first 100 samples: |
| | | sentence_0 | sentence_1 | sentence_2 | |
| |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | modality | text | text | text | |
| | details | <ul><li>min: 18 tokens</li><li>mean: 46.5 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 66.11 tokens</li><li>max: 242 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 84.2 tokens</li><li>max: 256 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | sentence_2 | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------| |
| | <code>As a professional, I feel constantly burnt out, always chasing the next promotion or project. I've lost touch with why I even started, and joy seems like a distant memory. Is there a way to reconnect with my passion?</code> | <code>yaṃ labdhvā cāparaṃ lābhaṃ manyate nādhikaṃ tataḥ \| yasmin sthito na duḥkhena guruṇāpi vicālyate \|\|22\|\| taṃ vidyād duḥkha-saṃyoga-viyogaṃ yoga-saṃjñitam \| sa niścayena yoktavyo yogo 'nirviṇṇa-cetasā \|\|23\|\|</code> | <code>yaṃ hi na vyathayanty ete puruṣaṃ puruṣarṣabha \| sama-duḥkha-sukhaṃ dhīraṃ so 'mṛtatvāya kalpate \|\|15\|\|</code> | |
| | <code>My teenage son is rebelling and pushing me away. I feel like I'm losing him. What can I do?</code> | <code>ayaneṣu ca sarveṣu yathābhāgam avasthitāḥ \| bhīṣmam evābhirakṣantu bhavantaḥ sarva eva hi \|\|11\|\|</code> | <code>acchedyo 'yam adāhyo 'yam akledyo 'śoṣya eva ca \| nityaḥ sarva-gataḥ sthāṇur acalo 'yaṃ sanātanaḥ \|\|24\|\|</code> | |
| | <code>I'm constantly worried about the future – what if my plans fail? What if things don't go my way? This anxiety paralyzes me and prevents me from acting.</code> | <code>yajñadānatapaḥkarma na tyājyaṃ kāryam eva tat \| yajño dānaṃ tapaś caiva pāvanāni manīṣiṇām \|\|5\|\|</code> | <code>ahiṃsā samatā tuṣṭis tapo dānaṃ yaśo 'yaśaḥ \| bhavanti bhāvā bhūtānāṃ matta eva pṛthagvidhāḥ \|\|5\|\|</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) 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`: 2 |
| - `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`: 2 |
| - `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`: 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`: {'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`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 1.6447 | 500 | 2.8599 | |
|
|
|
|
| ### Training Time |
| - **Training**: 10.0 minutes |
|
|
| ### Framework Versions |
| - Python: 3.11.12 |
| - Sentence Transformers: 5.5.1 |
| - Transformers: 5.12.1 |
| - PyTorch: 2.12.0+cu130 |
| - Accelerate: 1.14.0 |
| - Datasets: 5.0.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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