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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:40374
  - loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large
widget:
  - source_sentence: यथोवाच भगवान् धन्वन्तरिः ||२||
    sentences:
      - >-
        **Ashtanga Hridayam, Uttara Sthana, chapter 22, sutra 106**


        **Sutra**:

        पटोल-निम्ब-यष्ट्य्-आह्व-वासा-जात्य्-अरिमेदसाम् । खदिरस्य वरायाश् च पृथग्
        एवं प्रकल्पना ॥ १०६ ॥


        **English Transliteration**:

        paṭola-nimba-yaṣṭy-āhva-vāsā-jāty-arimedasām | khadirasya varāyāś ca
        pṛthag evaṁ prakalpanā || 106 ||


        **English Translation**:

        Thus, a separate preparation should be made from patola, nimba,
        licorice, vasa, jati, arimedasa, khadira, and vara.
      - |-
        **Susrut Samhita, Sharira Sthana, chapter 9, sutra 2**

        **Sutra**:
        यथोवाच भगवान् धन्वन्तरिः ||२||

        **English Transliteration**:
        yathovāca bhagavān dhanvantariḥ ||2||

        **English Translation**:
        Thus spoke the venerable Dhanvantari.
      - >-
        **Susrut Samhita, Chikitsa Sthana, chapter 24, sutra 85**


        **Sutra**:

        सुखं वातं प्रसेवेत ग्रीष्मे शरदि मानवः | निवातं ह्यायुषे सेव्यमारोग्याय
        च सर्वदा ||८५||


        **English Transliteration**:

        sukhaṃ vātaṃ praseveta grīṣme śaradi mānavaḥ | nivātaṃ hyāyuṣe
        sevyamārogyāya ca sarvadā ||85||


        **English Translation**:

        A person should enjoy pleasant wind in summer and autumn. Absence of
        wind is always beneficial for longevity and health.
  - source_sentence: >-
      विशीर्यते कूर्चकस्तु दन्तकाष्ठगते विषे | जिह्वादन्तौष्ठमांसानां
      श्वयथुश्चोपजायते ||४८||
    sentences:
      - >-
        **Susrut Samhita, Chikitsa Sthana, chapter 28, sutra 26**


        **Sutra**:

        पाप्मानं नाशयन्त्येता दद्युश्चौषधयः श्रियम् | कुर्युर्नागबलं चापि
        मनुष्यममरोपमम् ||२६||


        **English Transliteration**:

        pāpmānaṃ nāśayantyetā dadyuścauṣadhayaḥ śriyam | kuryurnāgabalaṃ cāpi
        manuṣyamamaropamam ||26||


        **English Translation**:

        These herbs destroy sin, bestow prosperity, and also create strength
        like that of serpents, making a human being comparable to the gods.
      - >-
        **Susrut Samhita, Kalpa Sthana, chapter 1, sutra 48**


        **Sutra**:

        विशीर्यते कूर्चकस्तु दन्तकाष्ठगते विषे | जिह्वादन्तौष्ठमांसानां
        श्वयथुश्चोपजायते ||४८||


        **English Transliteration**:

        viśīryate kūrcakastu dantakāṣṭhagate viṣe | jihvādantāuṣṭhamāṃsānāṃ
        śvayathuścopajāyate ||48||


        **English Translation**:

        When poison is present in the tooth-stick, the brush-like end
        disintegrates, and swelling arises in the tongue, teeth, lips, and gums.
      - >-
        **Charak-Samhita, chikitsa sthana, chapter 2, sutra 38**


        **Sutra**:

        गत्वा स्नात्वा पयः पीत्वा रसं वाऽनु शयीत ना| तथाऽस्याप्यायते भूयः शुक्रं
        च बलमेव च||३८||


        **English Transliteration**:

        gatvā snātvā payaḥ pītvā rasaṃ vā'nu śayīta nā| tathā'syāpyāyate bhūyaḥ
        śukraṃ ca balameva ca||38||


        **English Translation**:

        After intercourse, one should bathe, drink milk or juice, and then not
        sleep immediately. Thus, his semen, strength, and nourishment are
        increased again.
  - source_sentence: >-
      कृच्छ्रोन्मीले पुराणाज्यं द्राक्षा-कल्काम्बु-साधितम् । स-सितं योजयेत्
      स्निग्धं नस्य-धूमाञ्जनादि च ॥ १ ॥
    sentences:
      - >-
        **Ashtanga Hridayam, Uttara Sthana, chapter 9, sutra 1**


        **Sutra**:

        कृच्छ्रोन्मीले पुराणाज्यं द्राक्षा-कल्काम्बु-साधितम् । स-सितं योजयेत्
        स्निग्धं नस्य-धूमाञ्जनादि च ॥ १ ॥


        **English Transliteration**:

        kṛcchronmīle purāṇājyaṃ drākṣā-kalkāmbu-sādhitam | sa-sitaṃ yojayet
        snigdhaṃ nasya-dhūmāñjanādi ca || 1 ||


        **English Translation**:

        In difficult opening of the eyes, old ghee, processed with
        grape-paste-water; with sugar, apply it smoothly, nasal drops,
        fumigation, collyrium, and so on.
      - >-
        **Charak-Samhita, chikitsa sthana, chapter 30, sutra 339**


        **Sutra**:

        देशे देशे च यत् सात्म्यं यथा वैद्योऽपराध्यति| चिकित्सा चापि निर्दिष्टा
        दोषाणां गूढचारिणाम्||३३९||


        **English Transliteration**:

        dēśē dēśē ca yat sātmyaṁ yathā vaidyōparādhyati| cikitsā cāpi nirdiṣṭā
        dōṣāṇāṁ gūḍhacāriṇām||339||


        **English Translation**:

        The suitability for different regions, how a physician errs, and the
        treatment of hidden diseases are prescribed.
      - >-
        **Ashtanga Hridayam, Uttara Sthana, chapter 17, sutra 14**


        **Sutra**:

        खादन्तो जन्तवः कुर्युस् तीव्रां स कृमि-कर्णकः । श्रोत्र-कण्डूयनाज् जाते
        क्षते स्यात् पूर्व-लक्षणः ॥ १४ ॥


        **English Transliteration**:

        khādanto jantavaḥ kuryus tīvrāṃ sa kṛmi-karṇakaḥ | śrotra-kaṇḍūyanāj
        jāte kṣate syāt pūrva-lakṣaṇaḥ || 14 ||


        **English Translation**:

        Biting creatures cause intense pain; that is a worm-infested ear; from
        scratching the ear, when a wound arises, the previous symptoms manifest.
  - source_sentence: >-
      श्वासः कासः प्रतिश्यायो मुखशोषोऽतिपार्श्वरुक्| कफहीने पित्तमध्ये लिङ्गं
      वाताधिके मतम्||१०१||
    sentences:
      - >-
        **Susrut Samhita, Uttara tantra, chapter 42, sutra 80**


        **Sutra**:

        वायुः प्रकुपितः कोष्ठे शूलं सञ्जनयेद्भृशम् | निरुच्छ्वासी भवेत्तेन
        वेदनापीडितो नरः ||८०||


        **English Transliteration**:

        vāyuḥ prakupitaḥ koṣṭhe śūlaṃ sañjanayedbhṛśam | nirucchvāsī bhavettena
        vedanāpīḍito naraḥ ||80||


        **English Translation**:

        Aggravated Vata in the abdomen intensely generates pain (shula). Due to
        that pain, the person becomes breathless and afflicted by suffering.
      - >-
        **Charak-Samhita, chikitsa sthana, chapter 3, sutra 101**


        **Sutra**:

        श्वासः कासः प्रतिश्यायो मुखशोषोऽतिपार्श्वरुक्| कफहीने पित्तमध्ये लिङ्गं
        वाताधिके मतम्||१०१||


        **English Transliteration**:

        śvāsaḥ kāsaḥ pratiśyāyo mukhaśoṣo'tipārśvaruk| kaphahīne pittamadhye
        liṅgaṃ vātādhike matam||101||


        **English Translation**:

        Shortness of breath, cough, coryza (common cold), dryness of the mouth,
        and severe pain in the sides are the signs of increased Vata, with
        diminished Kapha and moderate Pitta.
      - >-
        **Charak-Samhita, chikitsa sthana, chapter 23, sutra 86**


        **Sutra**:

        आनद्धे गुदलेपो योनौ लेपश्च मूढगर्भाणाम्| मूर्च्छार्तिषु च ललाटे
        प्रलेपनमाहुः प्रधानतमम्||८६||


        **English Transliteration**:

        ānaddhe gudalepo yonau lepaśca mūḍhagarbhāṇām| mūrchārtisu ca lalāṭe
        pralepanamāhuḥ pradhānatamam||86||


        **English Translation**:

        It is said that this is an excellent application for flatulence, as a
        vaginal application for obstructed labor, and as a paste on the forehead
        for fainting and pain.
  - source_sentence: >-
      वातातपाध्व-यानादि-परिहार्येष्व् अ-यन्त्रणम् । प्रयोज्यं सु-कुमाराणाम्
      ईश्वराणाम् सुखात्मनाम् ॥ ४५ ॥
    sentences:
      - >-
        **Ashtanga Hridayam, Sutra Sthana, Sutra Sthana, chapter 6, sutra 129**


        **Sutra**:

        गुर्व् आम्रं वात-जित् पक्वं स्वाद्व् अम्लं कफ-शुक्र-कृत् । वृक्षाम्लं
        ग्राहि रूक्षोष्णं वात-श्लेष्म-हरं लघु ॥ १२९ ॥


        **English Transliteration**:

        gurv āmraṃ vāta-jit pakvaṃ svādv amlaṃ kapha-śukra-kṛt । vṛkṣāmlaṃ grāhi
        rūkṣoṣṇaṃ vāta-śleṣma-haraṃ laghu ॥ 129 ॥


        **English Translation**:

        Heavy mango vata-conquering ripe sweet-sour kapha-semen-doing. Garcinia
        astringent dry-hot vata-phlegm-removing light.
      - >-
        **Ashtanga Hridayam, Chikitsa Sthana, chapter 13, sutra 45**


        **Sutra**:

        वातातपाध्व-यानादि-परिहार्येष्व् अ-यन्त्रणम् । प्रयोज्यं सु-कुमाराणाम्
        ईश्वराणाम् सुखात्मनाम् ॥ ४५ ॥


        **English Transliteration**:

        vātātapādhva-yānādi-parihāryeṣv a-yantraṇam | prayojyaṃ su-kumārāṇām
        īśvarāṇām sukhātmanām || 45 ||


        **English Translation**:

        Without restrictions regarding avoidance of wind, sun, travel, etc., it
        can be used by delicate, wealthy, and happy individuals.
      - >-
        **Ashtanga Hridayam, Sutra Sthana, chapter 22, sutra 34**


        **Sutra**:

        कच-सदन-सित-त्व-पिञ्जर-त्वं परिफुटनं शिरसः समीर-रोगान् । जयति
        जनयतीन्द्रिय-प्रसादं स्वर-हनु-मूर्द्ध-बलं च मूर्द्ध-तैलम् ॥ ३४ ॥


        **English Transliteration**:

        kaca-sadana-sita-tva-piñjara-tvaṃ parisphuṭanaṃ śirasaḥ samīra-rogān ।
        jayati janayatīndriya-prasādaṃ svara-hanu-mūrddha-balaṃ ca
        mūrddha-tailam ॥ 34 ॥


        **English Translation**:

        Hair-falling-white-ness-yellowish-ness splitting of head wind-diseases
        overcomes generates sense-organ-pleasure voice-jaw-head-strength and
        head-oil.
datasets:
  - vrnP66/Inhouse_Devanagari
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on intfloat/multilingual-e5-large
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: Embedding Dataset Dev
          type: Embedding_Dataset_Dev
        metrics:
          - type: cosine_accuracy
            value: 0.9998018741607666
            name: Cosine Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli test
          type: all-nli-test
        metrics:
          - type: cosine_accuracy
            value: 0.9996037483215332
            name: Cosine Accuracy

SentenceTransformer based on intfloat/multilingual-e5-large

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the inhouse_devanagari dataset. It maps sentences & paragraphs to a 1024-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-large
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- inhouse_devanagari

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, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
queries = [
    "\u0935\u093e\u0924\u093e\u0924\u092a\u093e\u0927\u094d\u0935-\u092f\u093e\u0928\u093e\u0926\u093f-\u092a\u0930\u093f\u0939\u093e\u0930\u094d\u092f\u0947\u0937\u094d\u0935\u094d \u0905-\u092f\u0928\u094d\u0924\u094d\u0930\u0923\u092e\u094d \u0964 \u092a\u094d\u0930\u092f\u094b\u091c\u094d\u092f\u0902 \u0938\u0941-\u0915\u0941\u092e\u093e\u0930\u093e\u0923\u093e\u092e\u094d \u0908\u0936\u094d\u0935\u0930\u093e\u0923\u093e\u092e\u094d \u0938\u0941\u0916\u093e\u0924\u094d\u092e\u0928\u093e\u092e\u094d \u0965 \u096a\u096b \u0965",
]
documents = [
    '**Ashtanga Hridayam, Chikitsa Sthana, chapter 13, sutra 45**\n\n**Sutra**:\nवातातपाध्व-यानादि-परिहार्येष्व् अ-यन्त्रणम् । प्रयोज्यं सु-कुमाराणाम् ईश्वराणाम् सुखात्मनाम् ॥ ४५ ॥\n\n**English Transliteration**:\nvātātapādhva-yānādi-parihāryeṣv a-yantraṇam | prayojyaṃ su-kumārāṇām īśvarāṇām sukhātmanām || 45 ||\n\n**English Translation**:\nWithout restrictions regarding avoidance of wind, sun, travel, etc., it can be used by delicate, wealthy, and happy individuals.',
    '**Ashtanga Hridayam, Sutra Sthana, chapter 22, sutra 34**\n\n**Sutra**:\nकच-सदन-सित-त्व-पिञ्जर-त्वं परिफुटनं शिरसः समीर-रोगान् । जयति जनयतीन्द्रिय-प्रसादं स्वर-हनु-मूर्द्ध-बलं च मूर्द्ध-तैलम् ॥ ३४ ॥\n\n**English Transliteration**:\nkaca-sadana-sita-tva-piñjara-tvaṃ parisphuṭanaṃ śirasaḥ samīra-rogān । jayati janayatīndriya-prasādaṃ svara-hanu-mūrddha-balaṃ ca mūrddha-tailam ॥ 34 ॥\n\n**English Translation**:\nHair-falling-white-ness-yellowish-ness splitting of head wind-diseases overcomes generates sense-organ-pleasure voice-jaw-head-strength and head-oil.',
    '**Ashtanga Hridayam, Sutra Sthana, Sutra Sthana, chapter 6, sutra 129**\n\n**Sutra**:\nगुर्व् आम्रं वात-जित् पक्वं स्वाद्व् अम्लं कफ-शुक्र-कृत् । वृक्षाम्लं ग्राहि रूक्षोष्णं वात-श्लेष्म-हरं लघु ॥ १२९ ॥\n\n**English Transliteration**:\ngurv āmraṃ vāta-jit pakvaṃ svādv amlaṃ kapha-śukra-kṛt । vṛkṣāmlaṃ grāhi rūkṣoṣṇaṃ vāta-śleṣma-haraṃ laghu ॥ 129 ॥\n\n**English Translation**:\nHeavy mango vata-conquering ripe sweet-sour kapha-semen-doing. Garcinia astringent dry-hot vata-phlegm-removing light.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7942, 0.0831, 0.0912]])

Evaluation

Metrics

Triplet

Datasets: Embedding_Dataset_Dev and all-nli-test
Evaluated with TripletEvaluator

Metric	Embedding_Dataset_Dev	all-nli-test
cosine_accuracy	0.9998	0.9996

Training Details

Training Dataset

inhouse_devanagari

Dataset: inhouse_devanagari at 9076844
Size: 40,374 training samples
Columns: query, positive_pair, and negative_pair

Approximate statistics based on the first 1000 samples:

	query	positive_pair	negative_pair
type	string	string	string
details	min: 11 tokens mean: 55.91 tokens max: 512 tokens	min: 78 tokens mean: 193.14 tokens max: 512 tokens	min: 77 tokens mean: 192.85 tokens max: 512 tokens

Samples:

query	positive_pair	negative_pair
`नैते सृती पार्थ जानन्योगी मुह्यति कश्चन। तस्मात्सर्वेषु कालेषु योगयुक्तो भवार्जुन।।8.27।।`	Shloka: नैते सृती पार्थ जानन्योगी मुह्यति कश्चन। तस्मात्सर्वेषु कालेषु योगयुक्तो भवार्जुन।।8.27।। Transliteration: naite sṛtī pārtha jānanyogī muhyati kaścana\| tasmātsarveṣu kāleṣu yogayukto bhavārjuna\|\|8.27\|\| English Translation by Shri Purohit Swami: O Arjuna! The saint knowing these paths is not confused. Therefore meditate perpetually. English Translation Of Sri Shankaracharya's Sanskrit Commentary By Swami Gambirananda: O son of Prtha, na kascana yogi, no yogi whosoever; janan, has known; ete srti, these two courses as described-that one leads to worldly life, and the other to Liberation; muhyati, becomes deluded. Tasmat, therefore; O Arjuna, bhava, be you; yoga-yuktah, steadfast in Yoga; sarvesu kalesu, at all times. Here about the greatness of that yoga:	Shloka: यज्ञार्थात्कर्मणोऽन्यत्र लोकोऽयं कर्मबन्धनः। तदर्थं कर्म कौन्तेय मुक्तसंगः समाचर।।3.9।। Transliteration: yajñārthātkarmaṇo'nyatra loko'yaṃ karmabandhanaḥ\| tadarthaṃ karma kaunteya muktasaṃgaḥ samācara\|\|3.9\|\| English Translation by Shri Purohit Swami: In this world people are fettered by action, unless it is performed as a sacrifice. Therefore, O Arjuna, let thy acts be done without attachment, as sacrifice only. English Translation Of Sri Shankaracharya's Sanskrit Commentary By Swami Gambirananda: Ayam, this; lokah, man, the one who is eligible for action; karma-bandhanah, becomes bound by actions- the person who has karma as his bondage (bandhana) is karma-bandhanah-; anyatra, other than; that karmanah, action; yajnarthat, meant for Got not by that meant for God. According to the Vedic text, 'Sacrifice is verily Visnu' (Tai. Sam. 1.7.4), yajnah means God; whatever is done for Him is yajnartham. Therefore, mukta-sangah, without being attached, being free fr...
`Specifically, in the shataponaka type, the physician should create wounds within the tracts. After these have healed, the remaining tracts should be treated.`	Susrut Samhita, Chikitsa Sthana, chapter 8, sutra 5 Sutra: विशेषतस्तु- नाड्यन्तरे व्रणान् कुर्याद्भिषक् तु शतपोनके \| ततस्तेषूपरूढेषु शेषा नाडीरुपाचरेत् \|\|५\|\| English Transliteration: viśeṣatastu- nāḍyantare vraṇān kuryādbhīṣak tu śataponake \| tatasteṣūparūḍheṣu śeṣā nāḍīrupācaret \|\|5\|\| English Translation: Specifically, in the shataponaka type, the physician should create wounds within the tracts. After these have healed, the remaining tracts should be treated.	Susrut Samhita, Uttara tantra, chapter 39, sutra 306 Sutra: चूर्णितैस्त्रिफलाश्यामात्रिवृत्पिप्पलिसंयुतैः \| सक्षौद्रः शर्करायुक्तो विरेकस्तु प्रशस्यते \|\|३०६\|\| English Transliteration: cūrṇitaistriphalāśyāmātrivṛtpippalisaṃyutaiḥ \| sakṣaudraḥ śarkarāyukto virekastu praśasyate \|\|306\|\| English Translation: A purgative (vireka) is recommended when prepared with powdered Triphala, Shyama, Trivrit, and Pippali, mixed with honey and sugar.
`अथ पुण्ये ऽह्नि संपूज्य पूज्यांस् तां प्रविशेच् छुचिः । तत्र संशोधनैः शुद्धः सुखी जात-बलः पुनः ॥ ८ ॥`	Ashtanga Hridayam, Uttara Sthana, chapter 39, sutra 8 Sutra: अथ पुण्ये ऽह्नि संपूज्य पूज्यांस् तां प्रविशेच् छुचिः । तत्र संशोधनैः शुद्धः सुखी जात-बलः पुनः ॥ ८ ॥ English Transliteration: atha puṇye 'hni saṃpūjya pūjyāṃs tāṃ praviśec chuchiḥ \| tatra saṃśodhanaiḥ śuddhaḥ sukhī jāta-balaḥ punaḥ \|\| 8 \|\| English Translation: Then, on an auspicious day, having worshipped the worshipful, the pure one should enter it; there, purified by cleansing therapies, he becomes happy and regains strength.	Ashtanga Hridayam, Uttara Sthana, chapter 40, sutra 82 Sutra: दीर्घ-जीवितम् आरोग्यं धर्मम् अर्थं सुखं यशः । पाठावबोधानुष्ठानैर् अधिगच्छत्य् अतो ध्रुवम् ॥ ८२ ॥ English Transliteration: dīrgha-jīvitam ārogyaṁ dharmam arthaṁ sukhaṁ yaśaḥ \| pāṭhāvabodhānuṣṭhānair adhigacchaty ato dhruvam \|\| 82 \|\| English Translation: Long life, health, righteousness, wealth, happiness, and fame, one attains surely through reading, understanding, and practicing this.

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

inhouse_devanagari

Dataset: inhouse_devanagari at 9076844
Size: 5,047 evaluation samples
Columns: query, positive_pair, and negative_pair

Approximate statistics based on the first 1000 samples:

	query	positive_pair	negative_pair
type	string	string	string
details	min: 10 tokens mean: 54.29 tokens max: 512 tokens	min: 72 tokens mean: 192.49 tokens max: 512 tokens	min: 78 tokens mean: 196.56 tokens max: 512 tokens

Samples:

query	positive_pair	negative_pair
`Marma-destroyed separately not-said their flesh-etc.-depending-on. Generally with-foreign-body but agitating by action with-pain.`	Ashtanga Hridayam, Sutra Sthana, chapter 28, sutra 17 Sutra: मर्म-नष्टं पृथङ् नोक्तं तेषां मांसादि-संश्रयात् । सामान्येन स-शल्यं तु क्षोभिण्या क्रियया स-रुक् ॥ १७ ॥ English Transliteration: marma-naṣṭaṃ pṛthaṅ noktaṃ teṣāṃ māṃsādi-saṃśrayāt । sāmānyena sa-śalyaṃ tu kṣobhiṇyā kriyayā sa-ruk ॥ 17 ॥ English Translation: Marma-destroyed separately not-said their flesh-etc.-depending-on. Generally with-foreign-body but agitating by action with-pain.	Ashtanga Hridayam, Chikitsa Sthana, chapter 6, sutra 34 Sutra: पञ्च-कोल-शठी-पथ्या-गुड-बीजाह्व-पौष्करम् । वारुणी-कल्कितं भृष्टं यमके लवणान्वितम् ॥ ३४ ॥ English Transliteration: pañca-kola-śaṭhī-pathyā-guḍa-bījāhva-pauṣkaram । vāruṇī-kalkitaṃ bhṛṣṭaṃ yamake lavaṇānvitam ॥ 34 ॥ English Translation: Five-kolas, shathi, pathya, jaggery, bija, and pushkara, ground with varuni, fried in clarified butter, and mixed with salt.
`प्राचीनामलकं चैव दोषघ्नं गरहारि च\| ऐङ्गुदं तिक्तमधुरं स्निग्धोष्णं कफवातजित्\|\|१४६\|\|`	Charak-Samhita, sutra sthana, chapter 27, sutra 146 Sutra: प्राचीनामलकं चैव दोषघ्नं गरहारि च\| ऐङ्गुदं तिक्तमधुरं स्निग्धोष्णं कफवातजित्\|\|१४६\|\| English Transliteration: prācīnāmalakaṃ caiva doṣaghnaṃ garahāri ca\| aiṅgudaṃ tiktamadhuraṃ snigdhoṣṇaṃ kaphavātajit\|\|146\|\| English Translation: Pracinamalaka eliminates the doshas and counteracts poison. Inguda is bitter and sweet, unctuous and hot, and conquers Kapha and Vata.	Charak-Samhita, chikitsa sthana, chapter 15, sutra 65 Sutra: कट्वजीर्णविदाह्यम्लक्षाराद्यैः पित्तमुल्बणम्\| अग्निमाप्लावयद्धन्ति जलं तप्तमिवानलम्\|\|६५\|\| English Transliteration: kaṭvajīrṇavidāhyamlākṣārādyaiḥ pittamulbaṇam\| agnimāplāvayaddhanti jalaṃ taptamivānalam\|\|65\|\| English Translation: Pitta (bile) aggravated by pungent, indigestible, burning, sour, alkaline, and other substances, overwhelms the agni (digestive fire) and destroys it, just as hot water extinguishes a fire.
`Vāta becomes aggravated by excessive consumption of dry food, overeating, exposure to easterly winds, dew, sexual intercourse, suppression of natural urges, exertion, and exercise.`	Charak-Samhita, siddhi sthana, chapter 9, sutra 74 Sutra: रूक्षात्यध्यशनात् पूर्ववातावश्यायमैथुनैः\| वेगसन्धारणायासव्यायामैः कुपितोऽनिलः\|\|७४\|\| English Transliteration: rūkṣātyadhyaśanāt pūrvavātāvaśyāyamaithunaiḥ\| vegasaṃdhāraṇāyāsavyāyāmaiḥ kupito'nilaḥ\|\|74\|\| English Translation: Vāta becomes aggravated by excessive consumption of dry food, overeating, exposure to easterly winds, dew, sexual intercourse, suppression of natural urges, exertion, and exercise.	Charak-Samhita, sharira sthana, chapter 4, sutra 4 Sutra: मातृतः पितृत आत्मतः सात्म्यतो रसतः सत्त्वत इत्येतेभ्यो भावेभ्यः समुदितेभ्यो गर्भः सम्भवति\| तस्य ये येऽवयवा यतो यतः सम्भवतः सम्भवन्ति तान् विभज्य मातृजादीनवयवान् पृथक् पृथगुक्तमग्रे\|\|४\|\| English Transliteration: mātṛtaḥ pitṛta ātmatas sāmyato rasataḥ sattvata ityetebhyo bhāvebhyaḥ samuditebhyo garbhaḥ sambhavati\| tasya ye ye'vayavā yato yataḥ sambhavataḥ sambhavanti tān vibhajya mātṛjādīnavayavān pṛthak pṛthaguktamagre\|\|4\|\| English Translation: The embryo originates from the combined factors of the mother, the father, the self, suitability, nutrition, and the mind. The specific components of it that originate from each of these sources will be described separately in the following sections, distinguishing the maternal and other components.

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 5e-05
weight_decay: 0.0
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: 0.1
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
bf16: False
fp16: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	Embedding_Dataset_Dev_cosine_accuracy	all-nli-test_cosine_accuracy
-1	-1	-	-	0.9990	-
0.0396	100	0.4702	0.0037	0.9996	-
0.0792	200	0.0087	0.0041	0.9992	-
0.1189	300	0.004	0.0041	0.9994	-
0.1585	400	0.0037	0.0038	0.9994	-
0.1981	500	0.0041	0.0037	0.9994	-
0.2377	600	0.0011	0.0025	0.9994	-
0.2773	700	0.0046	0.0027	0.9996	-
0.3170	800	0.0014	0.0024	0.9998	-
0.3566	900	0.0008	0.0025	0.9998	-
0.3962	1000	0.0044	0.0027	1.0	-
0.4358	1100	0.0015	0.0027	1.0	-
0.4754	1200	0.0033	0.0031	0.9998	-
0.5151	1300	0.0071	0.0047	0.9996	-
0.5547	1400	0.0055	0.0027	0.9998	-
0.5943	1500	0.0025	0.0027	0.9994	-
0.6339	1600	0.003	0.0026	0.9994	-
0.6735	1700	0.0015	0.0024	0.9994	-
0.7132	1800	0.0017	0.0032	0.9996	-
0.7528	1900	0.0041	0.0025	0.9998	-
0.7924	2000	0.0041	0.0022	0.9998	-
0.8320	2100	0.0048	0.0022	0.9998	-
0.8716	2200	0.0011	0.0023	0.9998	-
0.9113	2300	0.0038	0.0024	0.9996	-
0.9509	2400	0.0039	0.0022	0.9998	-
0.9905	2500	0.0052	0.0020	0.9998	-
-1	-1	-	-	-	0.9996

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.1
Transformers: 4.57.0
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.2.0
Tokenizers: 0.22.1

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}
}