This is a sentence-transformers model finetuned from jangedoo/all-MiniLM-L6-v2-nepali on the title_excerpt, ne_en, excerpt_paraphrase, nepali_triplets, stsb_en, stsb_ne, stsb_en_ne and stsb_ne_en datasets. It maps sentences & paragraphs to a 384-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: jangedoo/all-MiniLM-L6-v2-nepali
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Datasets:
- title_excerpt
- ne_en
- excerpt_paraphrase
- nepali_triplets
- stsb_en
- stsb_ne
- stsb_en_ne
- stsb_ne_en

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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("jangedoo/all-MiniLM-L6-v3-nepali")
# Run inference
sentences = [
    'कालोपत्रे भएपछि यस्तो बन्यो थानकोटको फ्लाइओभर (तस्वीरहरू)',
    'Thankot flyover looks like this after being tarred (photos)',
    '58 hybrid vehicles entered in 10 months',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Datasets: multi_lang_ir, en_ir and ne_ir
Evaluated with InformationRetrievalEvaluator

Metric	multi_lang_ir	en_ir	ne_ir
cosine_accuracy@10	0.9174	0.9606	0.8874
cosine_precision@10	0.0917	0.0961	0.0887
cosine_precision@50	0.0193	0.0197	0.0191
cosine_recall@10	0.9174	0.9606	0.8874
cosine_recall@50	0.967	0.9871	0.9532
cosine_ndcg@10	0.8412	0.9163	0.7886
cosine_mrr@10	0.8165	0.9017	0.7568
cosine_map@100	0.8192	0.9032	0.7604

Translation

Dataset: translation
Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.5392
trg2src_accuracy	0.575
mean_accuracy	0.5571

Triplet

Dataset: nepali_triplets
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "margin": { 3 "cosine": 0.1, 4 "dot": 0.1, 5 "manhattan": 0.1, 6 "euclidean": 0.1 7 } 8}

Metric	Value
cosine_accuracy	0.54

Semantic Similarity

Datasets: stsb_en and stsb_ne
Evaluated with EmbeddingSimilarityEvaluator

Metric	stsb_en	stsb_ne
pearson_cosine	0.8818	0.6782
spearman_cosine	0.8791	0.6788

Training Details

Training Datasets

title_excerpt

Dataset: title_excerpt
Size: 86,649 training samples
Columns: title and excerpt
Approximate statistics based on the first 1000 samples:
title excerpt
type string string
details
min: 7 tokens
mean: 28.1 tokens
max: 92 tokens
min: 24 tokens
mean: 73.46 tokens
max: 256 tokens

Samples:

title	excerpt
`Rare Cheer Pheasant population rises in Kaligandaki Basin`	`The population of the rare Cheer Pheasant has increased in the Kaligandaki River basin areas of Myagdi and Mustang, attributed to conservation efforts and anti-poaching measures.`
`विदेश पठाउने भन्दै १४ करोड १३ लाख ठगी, एकजना पक्राउ`	`वैदेशिक रोजगारीमा पठाउने भन्दै १४ करोड १३ लाख रुपैयाँ ठगी गरेको आरोपमा मकवानपुरका ५२ वर्षीय शम्भु दयाल अग्रवाल पक्राउ परेका छन्।`
`Longstanding road encroachment issue in Hetauda resolved`	`A major demolition drive in Hetauda cleared 530 illegal structures encroaching on key highways to reclaim road boundaries after years of legal disputes.`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

ne_en

Dataset: ne_en
Size: 3,765 training samples
Columns: title and translation
Approximate statistics based on the first 1000 samples:
title translation
type string string
details
min: 4 tokens
mean: 30.39 tokens
max: 84 tokens
min: 3 tokens
mean: 25.22 tokens
max: 78 tokens

Samples:

title	translation
`FWLD pushes for inclusive, non-discriminatory citizenship law`	`FWLD समावेशी, गैर-भेदभावरहित नागरिकता कानूनको लागि जोड दिन्छ`
`[object Object] Clouded leopard: A vanishing jewel of the forest`	`[object Object] बादलयुक्त चितुवा: जंगलको हराउने रत्न`
`सिटिजन्स सदाबहार इकाइमा आवेदन म्याद थप`	`Application deadline extended to Citizens Sadabahar Unit`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

excerpt_paraphrase

Dataset: excerpt_paraphrase
Size: 549 training samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 549 samples:
sentence1 sentence2
type string string
details
min: 22 tokens
mean: 77.0 tokens
max: 180 tokens
min: 26 tokens
mean: 82.48 tokens
max: 184 tokens

Samples:

sentence1	sentence2
`भरतपुर कारागारमा मौसम परिवर्तन र वर्षातका कारण २२ कैदीबन्दीलाई रुघा र ज्वरोको समस्या देखिएको छ र उनीहरूको छुट्टै उपचार भैरहेको छ।`	`मौसममा आएको बदलाव र वर्षाका कारण भरतपुर जेलमा २२ जना कैदीबन्दीलाई रुघाखोकी र ज्वरोले सताएको छ, जसका लागि उनीहरूको विशेष उपचार भइरहेको छ।`
`Heavy to very heavy rainfall is likely today in six provinces of Nepal due to the monsoon trough near its average position, with thunder and lightning expected in several regions.`	`Intense precipitation is anticipated this day across six Nepalese provinces, owing to the monsoon trough maintaining its usual course, and electrical storms are predicted in various areas.`
भोजपुरको रामप्रसाद राई गाउँपालिका–६ बैकुण्ठेका पाँच सामुदायिक विद्यालयका ४३८ विद्यार्थीलाई पोसाक वितरण गरिएको छ। वडाले विद्यालयबीच एकरुपता ल्याउने लक्ष्यले पोसाक, जुत्ता र टिसर्ट वितरण गरेको हो।	रामप्रसाद राई गाउँपालिका–६, भोजपुरको बैकुण्ठेस्थित पाँच सामुदायिक विद्यालयका ४३८ जना विद्यार्थीहरूलाई वडाले विद्यालयहरूमा एकरूपता कायम गर्नका लागि पोसाक, जुत्ता तथा टिसर्ट प्रदान गरेको छ।

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

nepali_triplets

Dataset: nepali_triplets
Size: 1,600 training samples
Columns: sentence, positive_sentence, and negative_sentence

Approximate statistics based on the first 1000 samples:

	sentence	positive_sentence	negative_sentence
type	string	string	string
details	min: 25 tokens mean: 79.05 tokens max: 207 tokens	min: 28 tokens mean: 80.26 tokens max: 256 tokens	min: 21 tokens mean: 63.02 tokens max: 184 tokens

Samples:

sentence	positive_sentence	negative_sentence
`भारतका रक्षा प्रमुख जनरल अनिल चौहानले चीन, पाकिस्तान र बंगलादेशको गठबन्धनलाई भारतको सुरक्षाका लागि ठूलो खतरा भएको बताएका छन्।`	`चीन, पाकिस्तान र बंगलादेशको संयुक्त गठबन्धनलाई भारतको सुरक्षा चुनौतीको रूपमा जनरल अनिल चौहानले औंल्याएका छन्।`	`जनरल अनिल चौहानले भारतको सुरक्षाका लागि चीन र पाकिस्तानको गठबन्धन भन्दा मात्र बंगलादेशलाई ठूलो खतरा भनेका छन्।`
`The Sarlahi District Court has granted bail to suspended Bagmati Municipality Mayor Bharat Bahadur Thapa on a Rs 5 million bond in connection with illegal forest resource extraction charges.`	`Bharat Bahadur Thapa, the suspended mayor of Bagmati Municipality, was released on bail by the Sarlahi District Court after posting a bond of Rs 5 million related to allegations of unlawful forest resource exploitation.`	`The Sarlahi District Court denied bail to Bharat Bahadur Thapa, the suspended mayor of Bagmati Municipality, in the case concerning illegal forest resource extraction.`
नेपाली युवा महिला फुटबल टोलीका प्रशिक्षक यामप्रसाद गुरुङले साफ यु-२० महिला च्याम्पियनसिपको उपाधि जित्ने आशा दिएका छन्। नेपालले जुलाईमा बंगलादेशमा हुने प्रतियोगितामा बलियो टोली बनाएर प्रतिस्पर्धा गर्नेछ।	यामप्रसाद गुरुङले साफ यु-२० महिला च्याम्पियनसिपमा नेपालको टोलीले शीर्ष स्थान हासिल गर्ने विश्वास व्यक्त गरेका छन्। आगामी जुलाईमा बंगलादेशमा आयोजना हुने प्रतियोगितामा नेपालले सशक्त टोली प्रस्तुत गर्ने तयारीमा छ।	`नेपाली युवा महिला फुटबल टोलीले आगामी साफ यु-२० महिला च्याम्पियनसिपमा कमजोर प्रदर्शन गर्ने सम्भावना व्यक्त गरिएको छ।`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.COSINE", 3 "triplet_margin": 0.2 4}

stsb_en

Dataset: stsb_en
Size: 5,710 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 10.03 tokens
max: 28 tokens
min: 5 tokens
mean: 9.96 tokens
max: 25 tokens
min: 0.0
mean: 0.45
max: 1.0

Samples:

sentence1	sentence2	score
`A plane is taking off.`	`An air plane is taking off.`	`1.0`
`A man is playing a large flute.`	`A man is playing a flute.`	`0.76`
`A man is spreading shreded cheese on a pizza.`	`A man is spreading shredded cheese on an uncooked pizza.`	`0.76`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

stsb_ne

Dataset: stsb_ne
Size: 5,710 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 21.82 tokens
max: 60 tokens
min: 6 tokens
mean: 21.96 tokens
max: 54 tokens
min: 0.0
mean: 0.45
max: 1.0

Samples:

sentence1	sentence2	score
`एउटा विमान उडिरहेको छ।`	`हवाई जहाज उडिरहेको छ।`	`1.0`
`एउटा मान्छे ठूलो बाँसुरी बजाइरहेको छ।`	`एउटा मान्छे बाँसुरी बजाउँदै छ।`	`0.76`
`एक मानिस पिज्जामा टुक्रा चिज फैलाउँदै छ।`	`एक जना मानिसले न पकाएको पिज्जामा टुक्रा पारेको चीज फैलाउँदै छ।`	`0.76`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

stsb_en_ne

Dataset: stsb_en_ne
Size: 5,710 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 10.03 tokens
max: 28 tokens
min: 6 tokens
mean: 21.96 tokens
max: 54 tokens
min: 0.0
mean: 0.45
max: 1.0

Samples:

sentence1	sentence2	score
`A plane is taking off.`	`हवाई जहाज उडिरहेको छ।`	`1.0`
`A man is playing a large flute.`	`एउटा मान्छे बाँसुरी बजाउँदै छ।`	`0.76`
`A man is spreading shreded cheese on a pizza.`	`एक जना मानिसले न पकाएको पिज्जामा टुक्रा पारेको चीज फैलाउँदै छ।`	`0.76`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

stsb_ne_en

Dataset: stsb_ne_en
Size: 5,710 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 21.82 tokens
max: 60 tokens
min: 5 tokens
mean: 9.96 tokens
max: 25 tokens
min: 0.0
mean: 0.45
max: 1.0

Samples:

sentence1	sentence2	score
`एउटा विमान उडिरहेको छ।`	`An air plane is taking off.`	`1.0`
`एउटा मान्छे ठूलो बाँसुरी बजाइरहेको छ।`	`A man is playing a flute.`	`0.76`
`एक मानिस पिज्जामा टुक्रा चिज फैलाउँदै छ।`	`A man is spreading shredded cheese on an uncooked pizza.`	`0.76`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Evaluation Datasets

title_excerpt

Dataset: title_excerpt
Size: 4,333 evaluation samples
Columns: title and excerpt
Approximate statistics based on the first 1000 samples:
title excerpt
type string string
details
min: 6 tokens
mean: 28.18 tokens
max: 77 tokens
min: 24 tokens
mean: 73.83 tokens
max: 183 tokens

Samples:

title	excerpt
`बडीमालिकाका सात वडा छाउपडी गोठमुक्त`	`बडीमालिका नगरपालिकाका सात वटा वडाहरु छाउपडी प्रथाबाट मुक्त घोषणा गरिएका छन्। ९० प्रतिशत घर छाउ गोठमुक्त भएपछि वडा नम्बर ८ लाई नयाँ छाउपडी मुक्त वडा घोषणा गरिएको छ।`
`What are Iran’s ballistic missile capabilities?`	`Iran possesses one of the largest ballistic missile arsenals in the Middle East, with missiles capable of reaching Israel and beyond. Recent developments include the introduction of hypersonic missiles and underground missile facilities, underscoring Iran's strategic deterrence ambitions.`
`पर्सामा ट्रयाक्टर दुर्घटनामा परेर दुईको मृत्यु, एक घाइते`	`पर्सामा ट्रयाक्टर दुर्घटनामा परेर दुई जनाको मृत्यु भएको छ भने एक जना घाइते भएका छन्।`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

ne_en

Dataset: ne_en
Size: 189 evaluation samples
Columns: title and translation
Approximate statistics based on the first 189 samples:
title translation
type string string
details
min: 7 tokens
mean: 29.78 tokens
max: 71 tokens
min: 5 tokens
mean: 23.54 tokens
max: 85 tokens

Samples:

title	translation
`House passes Federal Civil Service Bill`	`संघीय निजामती सेवा विधेयक संसदबाट पारित`
`घूस लेनदेन : कांग्रेसबाट निर्वाचित नागार्जुनका मेयर बस्नेतलाई ८ वर्ष जेल र २ करोड ३० लाख जरिवाना`	`Bribery transaction: Mayor Basnet of Nagarjuna, who was elected by the Congress, was sentenced to 8 years in prison and a fine of 23 million`
`कुलिङ पिरियडको कमजोरीको नैतिक जिम्मेवारी लिन्छु, दोषीमाथि कारवाही हुनुपर्छ : रामहरि खतिवडा`	`I take moral responsibility for the weakness of the cooling period, action should be taken against the guilty: Ramhari Khatiwada`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

excerpt_paraphrase

Dataset: excerpt_paraphrase
Size: 27 evaluation samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 27 samples:
sentence1 sentence2
type string string
details
min: 33 tokens
mean: 83.3 tokens
max: 141 tokens
min: 35 tokens
mean: 90.89 tokens
max: 167 tokens

Samples:

sentence1	sentence2
त्रिभुवन विश्वविद्यालय परीक्षा नियन्त्रण कार्यालयले दश वटा मुख्य सेवा अनलाइनमा ल्याएपछि विद्यार्थीले कार्यालय धाउनु नपर्ने व्यवस्था भएको छ। अब विद्यार्थीले घरबाटै अनलाइन आवेदन दिई प्रमाणपत्र प्राप्त गर्न सक्नेछन्।	त्रिभुवन विश्वविद्यालयको परीक्षा नियन्त्रण कार्यालयले दश प्रमुख सेवाहरू अनलाइनमा सारेपछि विद्यार्थीहरूले कार्यालय धाउनु पर्ने झन्झटबाट मुक्ति पाएका छन्। यसले गर्दा अब उनीहरूले घरबाटै अनलाइन आवेदन दिएर प्रमाणपत्रहरू प्राप्त गर्न सक्नेछन्।
`नेपाल मेडिसिटी अस्पताल ललितपुरमा पहिलो पटक कलेजो प्रत्यारोपण सफल रुपमा गरिएको छ, जहाँ ईश्वर कार्कीलाई उनका छोरा आयुष कार्कीले कलेजो दान गरेका छन्।`	`ललितपुरस्थित नेपाल मेडिसिटी अस्पतालमा पहिलो सफल कलेजो प्रत्यारोपण सम्पन्न भएको छ, जसमा आयुष कार्कीले आफ्ना बुबा ईश्वर कार्कीलाई कलेजो प्रदान गरेका थिए।`
`अमेरिकी राष्ट्रपति डोनाल्ड ट्रम्पले इजरायल र इरान बीच युद्धविराम कार्यान्वयनमा आएको बताएका छन् र दुवै देशलाई उल्लंघन नगर्न आग्रह गरेका छन्।`	`अमेरिकी राष्ट्रपति डोनाल्ड ट्रम्पले इजरायल र इरानबीच युद्धविराम लागू भएको घोषणा गर्दै दुवै पक्षलाई त्यसको पालना गर्न आग्रह गरेका छन्।`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

nepali_triplets

Dataset: nepali_triplets
Size: 200 evaluation samples
Columns: sentence, positive_sentence, and negative_sentence

Approximate statistics based on the first 200 samples:

	sentence	positive_sentence	negative_sentence
type	string	string	string
details	min: 25 tokens mean: 76.12 tokens max: 177 tokens	min: 27 tokens mean: 77.06 tokens max: 151 tokens	min: 21 tokens mean: 61.07 tokens max: 120 tokens

Samples:

sentence	positive_sentence	negative_sentence
`नेपालको सेयर बजारमा मंगलबार ४ अंकले गिरावट आएको छ र कारोबार रकम १३ अर्ब १८ करोडबाट ७ अर्ब ६५ करोडमा घटेको छ। पुरे इनर्जी लिमिटेडको सेयरमा सर्किट लागेको छ।`	`मंगलबार नेपाल स्टक मार्केटमा ४ अंकको कमी देखिएको छ भने कारोबार रकम १३ अर्ब १८ करोडबाट घटेर ७ अर्ब ६५ करोड पुगेको छ। पुरे इनर्जी लिमिटेडको सेयरमा सर्किट ब्रेकर लाग्न पुगेको छ।`	`नेपालको सेयर बजारमा मंगलबार ४ अंकले वृद्धि भएको छ र कारोबार रकम ७ अर्ब ६५ करोडबाट १३ अर्ब १८ करोडमा बढेको छ। पुरे इनर्जी लिमिटेडको सेयरमा कुनै सर्किट लागेको छैन।`
`नेपाल चेम्बर अफ कमर्सले सुन र बहुमूल्य पत्थरमा लगाइएको विलासिता कर र भ्याट कर पुनरवलोकन गर्न अर्थमन्त्री विष्णु प्रसाद पौडेललाई अनुरोध गर्यो।`	`अर्थमन्त्री विष्णु प्रसाद पौडेललाई नेपाल चेम्बर अफ कमर्सले सुन र बहुमूल्य पत्थरमा लाग्ने विलासिता कर र भ्याट करको समीक्षा गर्न आग्रह गरेको छ।`	`नेपाल चेम्बर अफ कमर्सले सुन र बहुमूल्य पत्थरमा लगाइएको करहरू बढाउन अर्थमन्त्री विष्णु प्रसाद पौडेलसँग अनुरोध गरेको छ।`
`The CIAA arrested the chief and an assistant of the Bhaktapur Land Revenue Office for accepting a bribe of Rs 2.45 million.`	`Authorities from the CIAA took into custody the head and a subordinate of Bhaktapur's Land Revenue Office on charges of receiving a Rs 2.45 million bribe.`	`The CIAA investigated the Bhaktapur Land Revenue Office chief and assistant for alleged negligence in land record management.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.COSINE", 3 "triplet_margin": 0.2 4}

stsb_en

Dataset: stsb_en
Size: 1,378 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 13.51 tokens
max: 43 tokens
min: 5 tokens
mean: 13.47 tokens
max: 42 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence1	sentence2	score
`A girl is styling her hair.`	`A girl is brushing her hair.`	`0.5`
`A group of men play soccer on the beach.`	`A group of boys are playing soccer on the beach.`	`0.72`
`One woman is measuring another woman's ankle.`	`A woman measures another woman's ankle.`	`1.0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

stsb_ne

Dataset: stsb_ne
Size: 1,378 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 7 tokens
mean: 29.79 tokens
max: 147 tokens
min: 7 tokens
mean: 30.01 tokens
max: 127 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence1	sentence2	score
`एउटी केटी आफ्नो कपाल स्टाइल गर्दै छ।`	`एउटी केटी आफ्नो कपाल माझ्दै छ।`	`0.5`
`पुरुषहरूको समूह समुद्र तटमा फुटबल खेल्दै छ।`	`केटाहरूको समूह समुद्र तटमा फुटबल खेल्दै छ।`	`0.72`
`एउटी महिलाले अर्की महिलाको घुँडा नाप्दै छिन्।`	`एउटी महिलाले अर्को महिलाको खुट्टा नाप्छिन्।`	`1.0`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

stsb_en_ne

Dataset: stsb_en_ne
Size: 1,378 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 13.51 tokens
max: 43 tokens
min: 7 tokens
mean: 30.01 tokens
max: 127 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence1	sentence2	score
`A girl is styling her hair.`	`एउटी केटी आफ्नो कपाल माझ्दै छ।`	`0.5`
`A group of men play soccer on the beach.`	`केटाहरूको समूह समुद्र तटमा फुटबल खेल्दै छ।`	`0.72`
`One woman is measuring another woman's ankle.`	`एउटी महिलाले अर्को महिलाको खुट्टा नाप्छिन्।`	`1.0`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

stsb_ne_en

Dataset: stsb_ne_en
Size: 1,378 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 7 tokens
mean: 29.79 tokens
max: 147 tokens
min: 5 tokens
mean: 13.47 tokens
max: 42 tokens
min: 0.0
mean: 0.5
max: 1.0

Samples:

sentence1	sentence2	score
`एउटी केटी आफ्नो कपाल स्टाइल गर्दै छ।`	`A girl is brushing her hair.`	`0.5`
`पुरुषहरूको समूह समुद्र तटमा फुटबल खेल्दै छ।`	`A group of boys are playing soccer on the beach.`	`0.72`
`एउटी महिलाले अर्की महिलाको घुँडा नाप्दै छिन्।`	`A woman measures another woman's ankle.`	`1.0`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
load_best_model_at_end: True
gradient_checkpointing: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 8
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: 2e-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: False
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: True
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: True
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	title excerpt loss	ne en loss	excerpt paraphrase loss	nepali triplets loss	stsb en loss	stsb ne loss	stsb en ne loss	stsb ne en loss	multi_lang_ir_cosine_ndcg@10	en_ir_cosine_ndcg@10	ne_ir_cosine_ndcg@10	translation_mean_accuracy	nepali_triplets_cosine_accuracy	stsb_en_spearman_cosine	stsb_ne_spearman_cosine
0.0553	100	3.1763	0.0438	0.2323	0.0001	0.1189	0.0389	6.6070	6.1735	6.2053	0.7926	0.8915	0.7251	0.4947	0.4750	0.8671	0.6586
0.1107	200	1.8472	0.0338	0.2238	0.0002	0.1076	0.0376	6.3000	5.8402	5.8531	0.8124	0.8999	0.7522	0.5020	0.5250	0.8700	0.6681
0.1660	300	2.1218	0.0285	0.2096	0.0002	0.1007	0.0362	6.3730	5.8704	5.7911	0.8131	0.9043	0.7497	0.5073	0.5450	0.8739	0.6651
0.2214	400	1.4009	0.0262	0.2184	0.0001	0.1035	0.0361	6.4156	5.8561	5.7325	0.8204	0.9082	0.7593	0.5053	0.5350	0.8758	0.6630
0.2767	500	2.4768	0.0243	0.2268	0.0001	0.1030	0.0362	6.2723	5.6653	5.5011	0.8255	0.9091	0.7669	0.4814	0.5100	0.8749	0.6685
0.3320	600	1.6207	0.0247	0.1964	0.0001	0.1078	0.0348	6.2414	5.5925	5.4461	0.8282	0.9106	0.7708	0.5	0.5	0.8765	0.6709
0.3874	700	2.317	0.0246	0.1865	0.0001	0.1012	0.0353	6.2366	5.5786	5.3861	0.8316	0.9106	0.7764	0.5166	0.5400	0.8758	0.6718
0.4427	800	1.0611	0.0236	0.2016	0.0001	0.0984	0.0344	6.3357	5.6502	5.4199	0.8330	0.9121	0.7775	0.5020	0.5600	0.8787	0.6732
0.4981	900	1.4653	0.0221	0.1887	0.0001	0.0990	0.0334	6.3109	5.5926	5.4254	0.8335	0.9130	0.7778	0.5153	0.5300	0.8788	0.6737
0.5534	1000	1.3721	0.0228	0.1501	0.0001	0.1021	0.0342	6.3623	5.5621	5.3895	0.8337	0.9145	0.7773	0.5505	0.5350	0.8794	0.6701
0.6087	1100	2.0056	0.0212	0.1564	0.0001	0.1022	0.0344	6.2683	5.5163	5.3413	0.8367	0.9147	0.7820	0.5332	0.5250	0.8790	0.6740
0.6641	1200	1.4577	0.0212	0.1500	0.0001	0.1002	0.0342	6.2664	5.4797	5.3139	0.8364	0.9146	0.7817	0.5418	0.5250	0.8787	0.6742
0.7194	1300	1.8814	0.0208	0.1645	0.0001	0.1013	0.0339	6.2017	5.4290	5.2550	0.8389	0.9154	0.7852	0.5232	0.5250	0.8794	0.6762
0.7748	1400	1.9753	0.0206	0.1562	0.0001	0.0997	0.0339	6.1869	5.3668	5.2240	0.8396	0.9153	0.7864	0.5398	0.5300	0.8794	0.6783
0.8301	1500	1.3772	0.0206	0.1478	0.0001	0.1022	0.0339	6.2240	5.3975	5.2144	0.8397	0.9155	0.7866	0.5471	0.5150	0.8792	0.6770
0.8854	1600	2.3584	0.0206	0.1465	0.0001	0.1009	0.0344	6.1776	5.3378	5.1625	0.8412	0.9162	0.7888	0.5445	0.5250	0.8791	0.6785
0.9408	1700	2.5933	0.0206	0.1444	0.0001	0.1001	0.0345	6.1674	5.3130	5.1515	0.8413	0.9160	0.7890	0.5511	0.5350	0.8791	0.6788
0.9961	1800	1.6188	0.0207	0.1420	0.0001	0.0996	0.0344	6.1701	5.3158	5.1587	0.8412	0.9163	0.7887	0.5571	0.5400	0.8792	0.6788
-1	-1	-	-	-	-	-	-	-	-	-	0.8412	0.9163	0.7886	0.5571	0.5400	0.8791	0.6788

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.57.2
PyTorch: 2.12.0
Accelerate: 1.13.0
Datasets: 4.8.5
Tokenizers: 0.22.2

Citation

BibTeX

If you use this model, please cite it as:

bibtex

@misc{subedi2026allminilml6v3nepali,
  author       = {Subedi, Sanjaya},
  title        = {all-MiniLM-L6-v3-nepali: A Nepali Sentence Transformer for Semantic Search and Similarity},
  year         = {2026},
  publisher    = {Hugging Face},
  journal      = {Hugging Face model repository},
  howpublished = {\url{https://huggingface.co/jangedoo/all-MiniLM-L6-v3-nepali}},
  note         = {Fine-tuned Sentence Transformer model for Nepali and English-Nepali semantic similarity, semantic search, paraphrase mining, clustering, and retrieval}
}