| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:4858 |
| - loss:MultipleNegativesRankingLoss |
| base_model: sanganaka/bge-m3-sanskritFT |
| widget: |
| - source_sentence: My partner has made a serious mistake that has deeply hurt our |
| relationship. I feel immense anger and betrayal, but also a deep love. I'm caught |
| between forgiving them to save the relationship or protecting myself by walking |
| away, and I don't know which choice will bring me peace. |
| sentences: |
| - hṛṣīkeśaṃ tadā vākyam idam āha mahīpate | senayor ubhayor madhye rathaṃ sthāpaya |
| me 'cyuta ||21|| yāvad etān nirīkṣe 'haṃ yoddhukāmān avasthitān | kair mayā saha |
| yoddhavyam asmin raṇasamudyame ||22|| yotsyamānān avekṣe 'haṃ ya ete 'tra samāgatāḥ |
| | dhārtarāṣṭrasya durbuddher yuddhe priyacikīrṣavaḥ ||23|| |
| - na caitad vidmaḥ kataran no garīyo yad vā jayema yadi vā no jayeyuḥ | yān eva |
| hatvā na jijīviṣāmas te 'vasthitāḥ pramukhe dhārtarāṣṭrāḥ ||6|| |
| - suhṛn-mitrāry-udāsīna-madhyastha-dveṣya-bandhuṣu | sādhuṣv api ca pāpeṣu sama-buddhir |
| viśiṣyate ||9|| |
| - 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|| |
| - kārpaṇya-doṣopahata-svabhāvaḥ pṛcchāmi tvāṃ dharma-saṃmūḍha-cetāḥ | yac chreyaḥ |
| syān niścitaṃ brūhi tan me śiṣyas te 'haṃ śādhi māṃ tvāṃ prapannam ||7|| |
| - aniṣṭam iṣṭaṃ miśraṃ ca trividhaṃ karmaṇaḥ phalam | bhavaty atyāgināṃ pretya na |
| tu saṃnyāsināṃ kvacit ||12|| |
| - 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|| |
| - nirmāna-mohā jita-saṅga-doṣā adhyātma-nityā vinivṛtta-kāmāḥ | dvandvair vimuktāḥ |
| sukha-duḥkha-saṃjñair gacchanty amūḍhāḥ padam avyayaṃ tat ||5|| |
| - source_sentence: I’ve accumulated so many possessions, a big house, fancy cars, |
| but I still feel empty and unfulfilled. Why does nothing seem to bring lasting |
| joy? |
| sentences: |
| - vīta-rāga-bhaya-krodhā man-mayā mām upāśritāḥ | bahavo jñāna-tapasā pūtā mad-bhāvam |
| āgatāḥ ||10|| |
| - mahātmānas tu māṃ pārtha daivīṃ prakṛtim āśritāḥ | bhajanty ananya-manaso jñātvā |
| bhūtādim avyayam ||13|| |
| - yadṛcchā-lābha-santuṣṭo dvandvātīto vimatsaraḥ | samaḥ siddhāv asiddhau ca kṛtvāpi |
| na nibadhyate ||22|| |
| - aneka-bāhūdara-vaktra-netraṃ paśyāmi tvā sarvato 'nanta-rūpam | nāntaṃ na madhyaṃ |
| na punas tavādiṃ paśyāmi viśveśvara viśvarūpa ||16|| |
| - sarva-karmāṇi manasā saṃnyasyāste sukhaṃ vaśī | nava-dvāre pure dehī naiva kurvan |
| na kārayan ||13|| |
| - amī hi tvā sura-saṃghā viśanti kecid bhītāḥ prāñjalayo gṛṇanti | svastīty uktvā |
| maharṣi-siddha-saṃghāḥ stuvanti tvāṃ stutibhiḥ puṣkalābhiḥ ||21|| |
| - mām upetya punar janma duḥkhālayam aśāśvatam | nāpnuvanti mahātmānaḥ saṃsiddhiṃ |
| paramāṃ gatāḥ ||15|| |
| - paras tasmāt tu bhāvo 'nyo 'vyakto 'vyaktāt sanātanaḥ | yaḥ sa sarveṣu bhūteṣu |
| naśyatsu na vinaśyati ||20|| |
| - source_sentence: I've lost someone incredibly dear to me, and the pain is unbearable. |
| I feel like a part of me is gone forever. How can I heal and find meaning amidst |
| this sorrow? |
| sentences: |
| - avyakto 'kṣara ity uktas tam āhuḥ paramāṃ gatim | yaṃ prāpya na nivartante tad |
| dhāma paramaṃ mama ||21|| |
| - jitātmanaḥ praśāntasya paramātmā samāhitaḥ | śītoṣṇa-sukha-duḥkheṣu tathā mānāpamānayoḥ |
| ||7|| |
| - 'ye tu sarvāṇi karmāṇi mayi saṃnyasya matparaḥ | |
|
|
| ananyenaiva yogena māṃ dhyāyanta upāsate |' |
| - hṛṣīkeśaṃ tadā vākyam idam āha mahīpate | senayor ubhayor madhye rathaṃ sthāpaya |
| me 'cyuta ||21|| yāvad etān nirīkṣe 'haṃ yoddhukāmān avasthitān | kair mayā saha |
| yoddhavyam asmin raṇasamudyame ||22|| yotsyamānān avekṣe 'haṃ ya ete 'tra samāgatāḥ |
| | dhārtarāṣṭrasya durbuddher yuddhe priyacikīrṣavaḥ ||23|| |
| - 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|| |
| - mām upetya punar janma duḥkhālayam aśāśvatam | nāpnuvanti mahātmānaḥ saṃsiddhiṃ |
| paramāṃ gatāḥ ||15|| |
| - yasmān nodvijate loko lokān nodvijate ca yaḥ | harṣāmarṣa-bhayodvegair mukto yaḥ |
| sa ca me priyaḥ ||15|| |
| - āyuḥ-sattva-balārogya-sukha-prīti-vivardhanāḥ | rasyāḥ snigdhāḥ sthirā hṛdyā āhārāḥ |
| sāttvika-priyāḥ ||8|| |
| - source_sentence: I've invested so much time and energy into a project at work, only |
| for it to be unexpectedly cancelled. I feel so frustrated and defeated, like all |
| my effort was pointless. How do I cope with this sense of futility? |
| sentences: |
| - athaitad apy aśakto 'si kartuṃ mad-yogam āśritaḥ | sarva-karma-phala-tyāgaṃ tataḥ |
| kuru yatātmavān ||11|| |
| - nirmāna-mohā jita-saṅga-doṣā adhyātma-nityā vinivṛtta-kāmāḥ | dvandvair vimuktāḥ |
| sukha-duḥkha-saṃjñair gacchanty amūḍhāḥ padam avyayaṃ tat ||5|| |
| - yadā viniyataṃ cittam ātmany evāvatiṣṭhate | niḥspṛhaḥ sarva-kāmebhyo yukta ity |
| ucyate tadā ||18|| |
| - naiva kiṃ cit karomīti yukto manyeta tattva-vit | paśyañ śṛṇvan spṛśañ jighrann |
| aśnan gacchan svapañ śvasan ||8|| pralapan visṛjan gṛhṇann unmiṣan nimiṣann api |
| | indriyāṇīndriyārtheṣu vartanta iti dhārayan ||9|| |
| - mayādhyakṣeṇa prakṛtiḥ sūyate sa-carācaram | hetunānena kaunteya jagad viparivartate |
| ||10|| |
| - śucau deśe pratiṣṭhāpya sthiram āsanam ātmanaḥ | nātyucchritaṃ nātinīcaṃ cailājinakuśottaram |
| ||11|| tatraikāgraṃ manaḥ kṛtvā yata-cittendriya-kriyaḥ | upaviśyāsane yuñjyād |
| yogam ātma-viśuddhaye ||12|| |
| - prayāṇa-kāle manasācalena bhaktyā yukto yoga-balena caiva | bhruvor madhye prāṇam |
| āveśya samyak sa taṃ paraṃ puruṣam upaiti divyam ||10|| |
| - kāryam ity eva yat karma niyataṃ kriyaterjuna | saṅgaṃ tyaktvā phalaṃ caiva sa |
| tyāgaḥ sāttviko mataḥ ||9|| |
| - source_sentence: I've recently suffered a great loss, and I feel abandoned and questioning |
| why such pain exists if there's a benevolent force. Where is the solace in such |
| suffering? |
| sentences: |
| - divi sūrya-sahasrasya bhaved yugapad utthitā | yadi bhāḥ sadṛśī sā syād bhāsas |
| tasya mahātmanaḥ ||12|| |
| - arjuna uvāca saṃnyāsasya mahābāho tattvam icchāmi veditum | tyāgasya ca hṛṣīkeśa |
| pṛthak keśiniṣūdana ||1|| |
| - paritrāṇāya sādhūnāṃ vināśāya ca duṣkṛtām | dharma-saṃsthāpanārthāya saṃbhavāmi |
| yuge yuge ||8|| |
| - paśyaitāṃ pāṇḍuputrāṇām ācārya mahatīṃ camūm | vyūḍhāṃ drupadaputreṇa tava śiṣyeṇa |
| dhīmatā ||3|| |
| - tataḥ śvetair hayair yukte mahati syandane sthitau | mādhavaḥ pāṇḍavaś caiva divyau |
| śaṅkhau pradadhmatuḥ ||14|| |
| - saṃnyāsas tu mahābāho duḥkham āptum ayogataḥ | yoga-yukto munir brahma nacireṇādhigacchati |
| ||6|| |
| - mahā-bhūtāny ahaṃkāro buddhir avyaktam eva ca | indriyāṇi daśaikaṃ ca pañca cendriya-gocarāḥ |
| ||5|| icchā dveṣaḥ sukhaṃ duḥkhaṃ saṃghātaś cetanā dhṛtiḥ | etat kṣetraṃ samāsena |
| sa-vikāram udāhṛtam ||6|| |
| - avyakto 'kṣara ity uktas tam āhuḥ paramāṃ gatim | yaṃ prāpya na nivartante tad |
| dhāma paramaṃ mama ||21|| |
| 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 recently suffered a great loss, and I feel abandoned and questioning why such pain exists if there's a benevolent force. Where is the solace in such suffering?", |
| 'paritrāṇāya sādhūnāṃ vināśāya ca duṣkṛtām | dharma-saṃsthāpanārthāya saṃbhavāmi yuge yuge ||8||', |
| 'tataḥ śvetair hayair yukte mahati syandane sthitau | mādhavaḥ pāṇḍavaś caiva divyau śaṅkhau pradadhmatuḥ ||14||', |
| ] |
| 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, 1.0000, 1.0000], |
| # [1.0000, 1.0000, 1.0000], |
| # [1.0000, 1.0000, 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>, <code>sentence_2</code>, <code>sentence_3</code>, <code>sentence_4</code>, <code>sentence_5</code>, <code>sentence_6</code>, <code>sentence_7</code>, and <code>sentence_8</code> |
| * Approximate statistics based on the first 100 samples: |
| | | sentence_0 | sentence_1 | sentence_2 | sentence_3 | sentence_4 | sentence_5 | sentence_6 | sentence_7 | sentence_8 | |
| |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | type | string | string | string | string | string | string | string | string | string | |
| | modality | text | text | text | text | text | text | text | text | text | |
| | details | <ul><li>min: 25 tokens</li><li>mean: 45.6 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 66.34 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 64.54 tokens</li><li>max: 242 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 58.37 tokens</li><li>max: 242 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 60.14 tokens</li><li>max: 242 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 63.36 tokens</li><li>max: 165 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 63.16 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 67.22 tokens</li><li>max: 242 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 60.1 tokens</li><li>max: 242 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | sentence_2 | sentence_3 | sentence_4 | sentence_5 | sentence_6 | sentence_7 | sentence_8 | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------| |
| | <code>I'm constantly anxious about what the future holds—will I succeed, will my loved ones be okay, will things fall apart? I can't seem to just live in the present.</code> | <code>ā brahma-bhuvanāl lokāḥ punar-āvartino 'rjuna \| mām upetya tu kaunteya punar-janma na vidyate \|\|16\|\|</code> | <code>manuṣyāṇāṃ sahasreṣu kaś cid yatati siddhaye \| yatatām api siddhānāṃ kaś cin māṃ vetti tattvataḥ \|\|3\|\|</code> | <code>tasmāt sarveṣu kāleṣu mām anusmara yudhya ca \| mayy arpitamanobuddhir mām evaiṣyasy asaṃśayaḥ \|\|7\|\|</code> | <code>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\|\|</code> | <code>na hi prapaśyāmi mamāpanudyād yac chokam ucchoṣaṇam indriyāṇām \| avāpya bhūmāv asapatnam ṛddhaṃ rājyaṃ surāṇām api cādhipatyam \|\|8\|\|</code> | <code>anudvega-karaṃ vākyaṃ satyaṃ priya-hitaṃ ca yat \| svādhyāyābhyasanaṃ caiva vāṅ-mayaṃ tapa ucyate \|\|15\|\|</code> | <code>traividyā māṃ somapāḥ pūta-pāpā yajñair iṣṭvā svar-gatiṃ prārthayante \| te puṇyam āsādya surendra-lokam aśnanti divyān divi deva-bhogān \|\|20\|\|</code> | <code>āyuḥ-sattva-balārogya-sukha-prīti-vivardhanāḥ \| rasyāḥ snigdhāḥ sthirā hṛdyā āhārāḥ sāttvika-priyāḥ \|\|8\|\|</code> | |
| | <code>I feel so much anger towards certain people who have wronged me or situations that have gone completely against my desires. It feels like they or life itself is conspiring against me. Why does this happen, and how can I let go of this rage?</code> | <code>na kartṛtvaṃ na karmāṇi lokasya sṛjati prabhuḥ \| na karma-phala-saṃyogaṃ svabhāvas tu pravartate \|\|14\|\|</code> | <code>ye yathā māṃ prapadyante tāṃs tathaiva bhajāmy aham \| mama vartmānuvartante manuṣyāḥ pārtha sarvaśaḥ \|\|11\|\|</code> | <code>kleśo 'dhikataras teṣām avyaktāsakta-cetasām \| avyaktā hi gatir duḥkhaṃ dehavadbhir avāpyate \|\|5\|\|</code> | <code>manuṣyāṇāṃ sahasreṣu kaś cid yatati siddhaye \| yatatām api siddhānāṃ kaś cin māṃ vetti tattvataḥ \|\|3\|\|</code> | <code>yadā yadā hi dharmasya glānir bhavati bhārata \| abhyutthānam adharmasya tadātmānaṃ sṛjāmy aham \|\|7\|\|</code> | <code>mac-cittā mad-gata-prāṇā bodhayantaḥ parasparam \| kathayantaś ca māṃ nityaṃ tuṣyanti ca ramanti ca \|\|9\|\|</code> | <code>aśāstra-vihitaṃ ghoraṃ tapyante ye tapo janāḥ \| dambhāhaṃkāra-saṃyuktāḥ kāma-rāga-balānvitāḥ \|\|5\|\| karśayantaḥ śarīra-sthaṃ bhūta-grāmam acetasaḥ \| māṃ caivāntaḥ-śarīra-sthaṃ tān viddhy āsura-niścayān \|\|6\|\|</code> | <code>vedāvināśinaṃ nityaṃ ya enam ajam avyayam \| kathaṃ sa puruṣaḥ pārtha kaṃ ghātayati hanti kam \|\|21\|\|</code> | |
| | <code>I've lost someone incredibly dear to me, and the pain is unbearable. I feel like a part of me is gone forever. How can I heal and find meaning amidst this sorrow?</code> | <code>ye tu sarvāṇi karmāṇi mayi saṃnyasya matparaḥ \|<br>ananyenaiva yogena māṃ dhyāyanta upāsate \|</code> | <code>āyuḥ-sattva-balārogya-sukha-prīti-vivardhanāḥ \| rasyāḥ snigdhāḥ sthirā hṛdyā āhārāḥ sāttvika-priyāḥ \|\|8\|\|</code> | <code>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\|\|</code> | <code>avyakto 'kṣara ity uktas tam āhuḥ paramāṃ gatim \| yaṃ prāpya na nivartante tad dhāma paramaṃ mama \|\|21\|\|</code> | <code>hṛṣīkeśaṃ tadā vākyam idam āha mahīpate \| senayor ubhayor madhye rathaṃ sthāpaya me 'cyuta \|\|21\|\| yāvad etān nirīkṣe 'haṃ yoddhukāmān avasthitān \| kair mayā saha yoddhavyam asmin raṇasamudyame \|\|22\|\| yotsyamānān avekṣe 'haṃ ya ete 'tra samāgatāḥ \| dhārtarāṣṭrasya durbuddher yuddhe priyacikīrṣavaḥ \|\|23\|\|</code> | <code>yasmān nodvijate loko lokān nodvijate ca yaḥ \| harṣāmarṣa-bhayodvegair mukto yaḥ sa ca me priyaḥ \|\|15\|\|</code> | <code>mām upetya punar janma duḥkhālayam aśāśvatam \| nāpnuvanti mahātmānaḥ saṃsiddhiṃ paramāṃ gatāḥ \|\|15\|\|</code> | <code>jitātmanaḥ praśāntasya paramātmā samāhitaḥ \| śītoṣṇa-sukha-duḥkheṣu tathā mānāpamānayoḥ \|\|7\|\|</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`: 4 |
| - `num_train_epochs`: 2 |
| - `per_device_eval_batch_size`: 4 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 4 |
| - `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`: 4 |
| - `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 | |
| |:------:|:----:|:-------------:| |
| | 0.4115 | 500 | 3.6841 | |
| | 0.8230 | 1000 | 3.5072 | |
| | 1.2346 | 1500 | 3.4757 | |
| | 1.6461 | 2000 | 3.4740 | |
|
|
|
|
| ### Training Time |
| - **Training**: 25.7 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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