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--- |
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language: |
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- nep |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:3385 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: jangedoo/all-MiniLM-L6-v2-nepali |
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widget: |
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- source_sentence: नागरिकता टोलीले सर्जमिनको क्रममा कस्तो व्यक्तिको मतदाता परिचयपत्रको |
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सक्कल प्रति जाँच गर्छ? |
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sentences: |
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- नागरिकता टोलीले सर्जमिनको क्रममा निवेदकको जन्म, बसोबास, र नाताको तथ्यको रेकर्ड |
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राख्छ। |
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- नागरिकता टोलीले सर्जमिनको क्रममा निवेदकको मतदाता परिचयपत्रको सक्कल प्रति जाँच |
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गर्छ। |
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- राहदानीको विद्युतीय अभिलेखमा राहदानी जारी भएको मिति र अवधि समाप्त हुने मिति राखिन्छ। |
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- source_sentence: नागरिकता टोलीले कस्तो अवस्थामा सर्जमिनको समयसीमा लम्ब्याउन सक्छ? |
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sentences: |
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- नागरिकता टोलीले सर्जमिनको क्रममा जन्मदर्ता, नागरिकता, र स्थानीय तहको सिफारिसको |
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मूल प्रति माग्न सक्छ। |
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- नागरिकता टोलीले सर्जमिनको क्रममा निवेदकको बसोबास भएको स्थानको नक्सा हेर्न सक्छ। |
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- नागरिकता टोलीले जटिल तथ्य वा थप प्रमाण आवश्यक भएमा सर्जमिनको समयसीमा लम्ब्याउन |
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सक्छ। |
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- source_sentence: नागरिकताको प्रमाणपत्रमा विवरण सच्याउन आवश्यक प्रमाण के-के हुन्? |
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sentences: |
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- नागरिकताको प्रमाणपत्रमा विवरण सच्याउन आवश्यक प्रमाणमा निवेदकसँग भएको सबुत प्रमाण |
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र आवश्यकता अनुसार साक्षी र सरजमिन समावेश हुन्छ। |
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- संवत् २०४६ साल चैत्र मसान्तसम्म नेपाल सरहदभित्र जन्म भई नेपालमा स्थायी रुपले बसोबास |
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गर्दै आएको व्यक्ति जन्मको आधारमा नेपालको नागरिक हुनेछ। |
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- नागरिकता निवेदनमा निवेदकको जन्म मिति विक्रम संवत् वा ईस्वी संवत्मा स्पष्ट रूपमा |
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उल्लेख गर्नुपर्छ। |
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- source_sentence: राहदानी कुन कुन अवस्थामा रद्द गरिन्छ? |
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sentences: |
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- विदेशी नागरिकता त्यागेर पुनः नेपाली नागरिकता कायम गर्न अनुसूची-११ बमोजिमको ढाँचामा |
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निवेदन दिनुपर्छ, जसमा पूरा नाम, थर, जन्मस्थान, जन्म मिति, उमेर, साविकको नागरिकता |
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नम्बर, जारी मिति, नागरिकताको किसिम, नेपालमा बसोबास गरेको मिति, हालको बसोबासको |
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स्थान, बाबुको नाम, थर, ठेगाना, नागरिकता नम्बर, दस्तखत, औंठाको छाप, र विदेशी नागरिकता |
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त्यागेको निस्सा उल्लेख हुनुपर्छ। |
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- राहदानी हराएको, च्यातिएको, प्रयोग हुन नसक्ने, अवधि सकिएको, वा बुझी नलिएको अवस्थामा |
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रद्द गरिन्छ। |
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- दफा ५ को उपदफा (४) बमोजिम अंगीकृत नागरिकता प्रमाणपत्र अनुसूची-८ बमोजिमको ढाँचामा |
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जारी गरिन्छ, जसमा नागरिकताको किसिम, पूरा नाम, थर, जन्मस्थान, जन्म मिति, लिङ्ग, |
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स्थायी वासस्थान, दुवै कान देखिने अटो साइजको फोटो, र निर्णय मिति उल्लेख हुन्छ। |
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- source_sentence: राहदानी रद्द गर्न कस्तो सत्यताको घोषणा चाहिन्छ? |
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sentences: |
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- नागरिकता टोलीले सर्जमिनको क्रममा निवेदकको बसोबास भएको स्थानको नक्सा हेर्न सक्छ। |
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- राहदानी रद्द गर्न निवेदकले उल्लेखित विवरण साँचो भएको र प्रचलित कानून बमोजिम अपराध |
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ठहरिने कुनै काम नगरेको सत्यताको घोषणा गर्नुपर्छ। |
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- नागरिकता टोलीले गलत तथ्य वा अपूर्ण जानकारी भएमा सर्जमिनको प्रतिवेदन रद्द गर्न |
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सक्छ। |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: sentenceTransformer_nepali_embedding |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 384 |
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type: dim_384 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.2891246684350133 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5013262599469496 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6153846153846154 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7771883289124668 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.2891246684350133 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.16710875331564987 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.12307692307692306 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07771883289124668 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.2891246684350133 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.5013262599469496 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.6153846153846154 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.7771883289124668 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.5114393487220035 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.42878931413414173 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.4378957928577126 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.29708222811671087 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5225464190981433 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6259946949602122 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7771883289124668 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.29708222811671087 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.17418213969938107 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.12519893899204243 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07771883289124668 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.29708222811671087 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.5225464190981433 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.6259946949602122 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.7771883289124668 |
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|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.5196017799940188 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.43912361584775383 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.44830863398887005 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.2891246684350133 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5039787798408488 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6127320954907162 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7771883289124668 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.2891246684350133 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.16799292661361626 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.12254641909814322 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07771883289124668 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.2891246684350133 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.5039787798408488 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.6127320954907162 |
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|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.7771883289124668 |
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|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.513425703936886 |
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|
name: Cosine Ndcg@10 |
|
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- type: cosine_mrr@10 |
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value: 0.43126815713022615 |
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name: Cosine Mrr@10 |
|
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- type: cosine_map@100 |
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value: 0.4397863110473721 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.28116710875331563 |
|
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name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
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value: 0.493368700265252 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
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value: 0.610079575596817 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.7639257294429708 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
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value: 0.28116710875331563 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
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value: 0.16445623342175067 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.12201591511936338 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.07639257294429708 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.28116710875331563 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.493368700265252 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.610079575596817 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.7639257294429708 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.5039737400654479 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.42297061176371525 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.43166547136933925 |
|
|
name: Cosine Map@100 |
|
|
--- |
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# sentenceTransformer_nepali_embedding |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jangedoo/all-MiniLM-L6-v2-nepali](https://huggingface.co/jangedoo/all-MiniLM-L6-v2-nepali) on the json dataset. 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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [jangedoo/all-MiniLM-L6-v2-nepali](https://huggingface.co/jangedoo/all-MiniLM-L6-v2-nepali) <!-- at revision 418f7cf08ecbbc2ff0e8460bb6eb6457291102df --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- json |
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- **Language:** nep |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
|
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model = SentenceTransformer("ritesh-07/fine_tuned_model_03") |
|
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# Run inference |
|
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sentences = [ |
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'राहदानी रद्द गर्न कस्तो सत्यताको घोषणा चाहिन्छ?', |
|
|
'राहदानी रद्द गर्न निवेदकले उल्लेखित विवरण साँचो भएको र प्रचलित कानून बमोजिम अपराध ठहरिने कुनै काम नगरेको सत्यताको घोषणा गर्नुपर्छ।', |
|
|
'नागरिकता टोलीले गलत तथ्य वा अपूर्ण जानकारी भएमा सर्जमिनको प्रतिवेदन रद्द गर्न सक्छ।', |
|
|
] |
|
|
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] |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
|
|
--> |
|
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|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
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|
|
You can finetune this model on your own dataset. |
|
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|
|
<details><summary>Click to expand</summary> |
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|
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|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Out-of-Scope Use |
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|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
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--> |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### Metrics |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_384` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 384 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.2891 | |
|
|
| cosine_accuracy@3 | 0.5013 | |
|
|
| cosine_accuracy@5 | 0.6154 | |
|
|
| cosine_accuracy@10 | 0.7772 | |
|
|
| cosine_precision@1 | 0.2891 | |
|
|
| cosine_precision@3 | 0.1671 | |
|
|
| cosine_precision@5 | 0.1231 | |
|
|
| cosine_precision@10 | 0.0777 | |
|
|
| cosine_recall@1 | 0.2891 | |
|
|
| cosine_recall@3 | 0.5013 | |
|
|
| cosine_recall@5 | 0.6154 | |
|
|
| cosine_recall@10 | 0.7772 | |
|
|
| **cosine_ndcg@10** | **0.5114** | |
|
|
| cosine_mrr@10 | 0.4288 | |
|
|
| cosine_map@100 | 0.4379 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_256` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 256 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.2971 | |
|
|
| cosine_accuracy@3 | 0.5225 | |
|
|
| cosine_accuracy@5 | 0.626 | |
|
|
| cosine_accuracy@10 | 0.7772 | |
|
|
| cosine_precision@1 | 0.2971 | |
|
|
| cosine_precision@3 | 0.1742 | |
|
|
| cosine_precision@5 | 0.1252 | |
|
|
| cosine_precision@10 | 0.0777 | |
|
|
| cosine_recall@1 | 0.2971 | |
|
|
| cosine_recall@3 | 0.5225 | |
|
|
| cosine_recall@5 | 0.626 | |
|
|
| cosine_recall@10 | 0.7772 | |
|
|
| **cosine_ndcg@10** | **0.5196** | |
|
|
| cosine_mrr@10 | 0.4391 | |
|
|
| cosine_map@100 | 0.4483 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_128` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 128 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.2891 | |
|
|
| cosine_accuracy@3 | 0.504 | |
|
|
| cosine_accuracy@5 | 0.6127 | |
|
|
| cosine_accuracy@10 | 0.7772 | |
|
|
| cosine_precision@1 | 0.2891 | |
|
|
| cosine_precision@3 | 0.168 | |
|
|
| cosine_precision@5 | 0.1225 | |
|
|
| cosine_precision@10 | 0.0777 | |
|
|
| cosine_recall@1 | 0.2891 | |
|
|
| cosine_recall@3 | 0.504 | |
|
|
| cosine_recall@5 | 0.6127 | |
|
|
| cosine_recall@10 | 0.7772 | |
|
|
| **cosine_ndcg@10** | **0.5134** | |
|
|
| cosine_mrr@10 | 0.4313 | |
|
|
| cosine_map@100 | 0.4398 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_64` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 64 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:----------| |
|
|
| cosine_accuracy@1 | 0.2812 | |
|
|
| cosine_accuracy@3 | 0.4934 | |
|
|
| cosine_accuracy@5 | 0.6101 | |
|
|
| cosine_accuracy@10 | 0.7639 | |
|
|
| cosine_precision@1 | 0.2812 | |
|
|
| cosine_precision@3 | 0.1645 | |
|
|
| cosine_precision@5 | 0.122 | |
|
|
| cosine_precision@10 | 0.0764 | |
|
|
| cosine_recall@1 | 0.2812 | |
|
|
| cosine_recall@3 | 0.4934 | |
|
|
| cosine_recall@5 | 0.6101 | |
|
|
| cosine_recall@10 | 0.7639 | |
|
|
| **cosine_ndcg@10** | **0.504** | |
|
|
| cosine_mrr@10 | 0.423 | |
|
|
| cosine_map@100 | 0.4317 | |
|
|
|
|
|
<!-- |
|
|
## 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 |
|
|
|
|
|
#### json |
|
|
|
|
|
* Dataset: json |
|
|
* Size: 3,385 training samples |
|
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | anchor | positive | |
|
|
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
|
| type | string | string | |
|
|
| details | <ul><li>min: 18 tokens</li><li>mean: 49.31 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 81.7 tokens</li><li>max: 256 tokens</li></ul> | |
|
|
* Samples: |
|
|
| anchor | positive | |
|
|
|:--------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>राहदानी नियमावली, २०७७ मा दस्तुर बुझाउने प्रक्रिया कस्तो छ?</code> | <code>राहदानी नियमावली, २०७७ मा दस्तुर तोकिएको बैङ्कमा बुझाई रसिद निवेदनमा संलग्न गर्नुपर्छ।</code> | |
|
|
| <code>दफा ३ को उपदफा (६) मा विदेशी नागरिकसँग विवाह गरेकी नेपाली महिलाको सन्तानले कसरी नागरिकता प्राप्त गर्छ?</code> | <code>दफा ३ को उपदफा (६) मा विदेशी नागरिकसँग विवाह गरेकी नेपाली महिला नागरिकबाट नेपालमा जन्मिएको व्यक्तिले, यदि निजको आमा र बाबु दुवै नेपाली नागरिक रहेछन् भने, वंशजको आधारमा नेपालको नागरिकता प्राप्त गर्नेछ।</code> | |
|
|
| <code>दफा ३ को उपदफा (४) मा कस्तो व्यवस्था थपिएको छ?</code> | <code>दफा ३ को उपदफा (४) मा थपिएको व्यवस्था अनुसार, संवत् २०७२ साल असोज ३ गतेभन्दा अघि जन्मको आधारमा नेपालको नागरिकता प्राप्त गरेको नागरिकको सन्तानले, यदि बाबु र आमा दुवै नेपालको नागरिक रहेछन् भने, निजको उमेर सोह्र वर्ष पूरा भएपछि वंशजको आधारमा नेपालको नागरिकता प्राप्त गर्नेछ।</code> | |
|
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"loss": "MultipleNegativesRankingLoss", |
|
|
"matryoshka_dims": [ |
|
|
384, |
|
|
256, |
|
|
128, |
|
|
64 |
|
|
], |
|
|
"matryoshka_weights": [ |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1 |
|
|
], |
|
|
"n_dims_per_step": -1 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: epoch |
|
|
- `per_device_train_batch_size`: 32 |
|
|
- `per_device_eval_batch_size`: 16 |
|
|
- `gradient_accumulation_steps`: 16 |
|
|
- `learning_rate`: 2e-05 |
|
|
- `num_train_epochs`: 4 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `bf16`: True |
|
|
- `tf32`: False |
|
|
- `load_best_model_at_end`: True |
|
|
- `optim`: adamw_torch_fused |
|
|
- `batch_sampler`: no_duplicates |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: epoch |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 32 |
|
|
- `per_device_eval_batch_size`: 16 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 16 |
|
|
- `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`: 4 |
|
|
- `max_steps`: -1 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `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 |
|
|
- `use_ipex`: False |
|
|
- `bf16`: True |
|
|
- `fp16`: False |
|
|
- `fp16_opt_level`: O1 |
|
|
- `half_precision_backend`: auto |
|
|
- `bf16_full_eval`: False |
|
|
- `fp16_full_eval`: False |
|
|
- `tf32`: False |
|
|
- `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} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch_fused |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `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`: False |
|
|
- `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`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | dim_384_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
|
| 1.0 | 7 | - | 0.4635 | 0.4673 | 0.4674 | 0.4406 | |
|
|
| 1.4528 | 10 | 2.6919 | - | - | - | - | |
|
|
| 2.0 | 14 | - | 0.4977 | 0.5140 | 0.4963 | 0.4759 | |
|
|
| 2.9057 | 20 | 1.0521 | - | - | - | - | |
|
|
| 3.0 | 21 | - | 0.5111 | 0.5242 | 0.5130 | 0.5017 | |
|
|
| **4.0** | **28** | **-** | **0.5114** | **0.5196** | **0.5134** | **0.504** | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.13 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.54.0 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.9.0 |
|
|
- Datasets: 4.0.0 |
|
|
- Tokenizers: 0.21.2 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MatryoshkaLoss |
|
|
```bibtex |
|
|
@misc{kusupati2024matryoshka, |
|
|
title={Matryoshka Representation Learning}, |
|
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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|
year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
|
|
``` |
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