pritamdeka commited on
Commit
2d852be
1 Parent(s): 10bdb7c

Add new SentenceTransformer model.

Browse files
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1
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+ datasets: []
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+ language: []
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+ library_name: sentence-transformers
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ - pearson_manhattan
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+ - spearman_manhattan
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+ - pearson_euclidean
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+ - spearman_euclidean
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+ - pearson_dot
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+ - spearman_dot
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+ - pearson_max
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+ - spearman_max
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+ pipeline_tag: sentence-similarity
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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:5749
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+ - loss:CosineSimilarityLoss
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+ widget:
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+ - source_sentence: আমি "... comoving মহাজাগতিক বিশ্ৰাম ফ্ৰেমৰ তুলনাত ... সিংহ নক্ষত্ৰমণ্ডলৰ
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+ ফালে কিছু 371 কিলোমিটাৰ প্ৰতি ছেকেণ্ডত" আগবাঢ়িছো.
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+ sentences:
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+ - বাস্কেটবল খেলুৱৈগৰাকীয়ে নিজৰ দলৰ হৈ পইণ্ট লাভ কৰিবলৈ ওলাইছে।
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+ - আন কোনো বস্তুৰ লগত আপেক্ষিক নহোৱা কোনো ‘ষ্টিল’ নাই।
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+ - এজনী ছোৱালীয়ে বতাহ বাদ্যযন্ত্ৰ বজায়।
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+ - source_sentence: চাৰিটা ল’ৰা-ছোৱালীয়ে ভঁৰালৰ জীৱ-জন্তুবোৰলৈ চাই আছে।
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+ sentences:
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+ - ডাইনিং টেবুল এখনৰ চাৰিওফালে বৃদ্ধৰ দল এটাই পোজ দিছে।
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+ - বিকিনি পিন্ধা চাৰিগৰাকী মহিলাই বিলত ভলীবল খেলি আছে।
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+ - ল’ৰা-ছোৱালীয়ে ভেড়া চাই।
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+ - source_sentence: ডালত বহি থকা দুটা টান ঈগল।
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+ sentences:
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+ - জাতৰ জেব্ৰা ডানিঅ’ অত্যন্ত কঠোৰ মাছ, ইহঁতক হত্যা কৰাটো প্ৰায় কঠিন।
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+ - এটা ডালত দুটা ঈগল বহি আছে।
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+ - নূন্যতম মজুৰিৰ আইনসমূহে কম দক্ষ, কম উৎপাদনশীল লোকক আটাইতকৈ বেছি আঘাত দিয়ে।
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+ - source_sentence: '"মই আচলতে যি বিচাৰিছো সেয়া হৈছে মুছলমান জনসংখ্যাৰ এটা অনুমান..."
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+ @ThanosK আৰু @T.E.D., এটা সামগ্ৰিক, সাধাৰণ জনসংখ্যাৰ অনুমান f.e.'
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+ sentences:
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+ - এগৰাকী মহিলাই সেউজীয়া পিঁয়াজ কাটি আছে।
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+ - তলত দিয়া কথাখিনি মোৰ কুকুৰ কাণৰ দৰে কপিৰ পৰা লোৱা হৈছে নিউ পেংগুইন এটলাছ অৱ মেডিভেল
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+ হিষ্ট্ৰীৰ।
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+ - আমাৰ দৰে সৌৰজগতৰ কোনো তাৰকাৰাজ্যৰ বাহিৰত থকাটো সম্ভৱ হ’ব পাৰে।
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+ - source_sentence: ইণ্টাৰনেট কেমেৰাৰ জৰিয়তে এগৰাকী ছোৱালীৰ লগত কথা পাতিলে মানুহজনে।
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+ sentences:
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+ - গছৰ শাৰী এটাৰ সন্মুখত পথাৰত ভেড়া চৰিছে।
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+ - এজন মানুহে গীটাৰ বজাই আছে।
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+ - ৱেবকেমৰ জৰিয়তে এগৰাকী ছোৱালীৰ সৈতে কথা পাতিছে এজন কিশোৰে।
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+ model-index:
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+ - name: SentenceTransformer based on pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: pritamdeka/stsb assamese translated dev
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+ type: pritamdeka/stsb-assamese-translated-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8103888874564235
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.808745256408391
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.7856524098322162
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.7931254692762979
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.787635055496797
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.7951615705258325
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.7706254928060731
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.7771019257164439
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.8103888874564235
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.808745256408391
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+ name: Spearman Max
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: pritamdeka/stsb assamese translated test
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+ type: pritamdeka/stsb-assamese-translated-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.7701562538442139
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7660618813636367
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.749425583772647
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.7529158472529595
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.7498757891992801
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.7531339468525071
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.7193336616396375
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.7151802549941848
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.7701562538442139
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.7660618813636367
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1](https://huggingface.co/pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1). It maps sentences & paragraphs to a 768-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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1](https://huggingface.co/pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1) <!-- at revision e1bd9f5cf02ff4ac84bb1d9d570a6d4aae689d51 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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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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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
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+ (1): Pooling({'word_embedding_dimension': 768, '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})
161
+ )
162
+ ```
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+
164
+ ## Usage
165
+
166
+ ### Direct Usage (Sentence Transformers)
167
+
168
+ First install the Sentence Transformers library:
169
+
170
+ ```bash
171
+ pip install -U sentence-transformers
172
+ ```
173
+
174
+ Then you can load this model and run inference.
175
+ ```python
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+ from sentence_transformers import SentenceTransformer
177
+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1-sts")
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+ # Run inference
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+ sentences = [
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+ 'ইণ্টাৰনেট কেমেৰাৰ জৰিয়তে এগৰাকী ছোৱালীৰ লগত কথা পাতিলে মানুহজনে।',
183
+ 'ৱেবকেমৰ জৰিয়তে এগৰাকী ছোৱালীৰ সৈতে কথা পাতিছে এজন কিশোৰে।',
184
+ 'এজন মানুহে গীটাৰ বজাই আছে।',
185
+ ]
186
+ embeddings = model.encode(sentences)
187
+ print(embeddings.shape)
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+ # [3, 768]
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+
190
+ # Get the similarity scores for the embeddings
191
+ similarities = model.similarity(embeddings, embeddings)
192
+ print(similarities.shape)
193
+ # [3, 3]
194
+ ```
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+
196
+ <!--
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+ ### Direct Usage (Transformers)
198
+
199
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
201
+ </details>
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+ -->
203
+
204
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
206
+
207
+ You can finetune this model on your own dataset.
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+
209
+ <details><summary>Click to expand</summary>
210
+
211
+ </details>
212
+ -->
213
+
214
+ <!--
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+ ### Out-of-Scope Use
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+
217
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
218
+ -->
219
+
220
+ ## Evaluation
221
+
222
+ ### Metrics
223
+
224
+ #### Semantic Similarity
225
+ * Dataset: `pritamdeka/stsb-assamese-translated-dev`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.8104 |
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+ | **spearman_cosine** | **0.8087** |
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+ | pearson_manhattan | 0.7857 |
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+ | spearman_manhattan | 0.7931 |
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+ | pearson_euclidean | 0.7876 |
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+ | spearman_euclidean | 0.7952 |
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+ | pearson_dot | 0.7706 |
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+ | spearman_dot | 0.7771 |
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+ | pearson_max | 0.8104 |
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+ | spearman_max | 0.8087 |
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+
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+ #### Semantic Similarity
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+ * Dataset: `pritamdeka/stsb-assamese-translated-test`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.7702 |
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+ | **spearman_cosine** | **0.7661** |
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+ | pearson_manhattan | 0.7494 |
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+ | spearman_manhattan | 0.7529 |
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+ | pearson_euclidean | 0.7499 |
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+ | spearman_euclidean | 0.7531 |
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+ | pearson_dot | 0.7193 |
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+ | spearman_dot | 0.7152 |
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+ | pearson_max | 0.7702 |
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+ | spearman_max | 0.7661 |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
261
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
263
+
264
+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
269
+
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+ ## Training Details
271
+
272
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `num_train_epochs`: 10
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `learning_rate`: 5e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 10
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: True
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: False
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+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
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+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
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+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
380
+ - `ray_scope`: last
381
+ - `ddp_timeout`: 1800
382
+ - `torch_compile`: False
383
+ - `torch_compile_backend`: None
384
+ - `torch_compile_mode`: None
385
+ - `dispatch_batches`: None
386
+ - `split_batches`: None
387
+ - `include_tokens_per_second`: False
388
+ - `include_num_input_tokens_seen`: False
389
+ - `neftune_noise_alpha`: None
390
+ - `optim_target_modules`: None
391
+ - `batch_eval_metrics`: False
392
+ - `eval_on_start`: False
393
+ - `batch_sampler`: batch_sampler
394
+ - `multi_dataset_batch_sampler`: proportional
395
+
396
+ </details>
397
+
398
+ ### Training Logs
399
+ | Epoch | Step | Training Loss | loss | pritamdeka/stsb-assamese-translated-dev_spearman_cosine | pritamdeka/stsb-assamese-translated-test_spearman_cosine |
400
+ |:----------:|:-------:|:-------------:|:----------:|:-------------------------------------------------------:|:--------------------------------------------------------:|
401
+ | 1.1111 | 100 | 0.0386 | 0.0324 | 0.8024 | - |
402
+ | 2.2222 | 200 | 0.0238 | 0.0316 | 0.8095 | - |
403
+ | 3.3333 | 300 | 0.0141 | 0.0316 | 0.8092 | - |
404
+ | 4.4444 | 400 | 0.0086 | 0.0319 | 0.8085 | - |
405
+ | **5.5556** | **500** | **0.0065** | **0.0314** | **0.8107** | **-** |
406
+ | 6.6667 | 600 | 0.005 | 0.0318 | 0.8088 | - |
407
+ | 7.7778 | 700 | 0.0044 | 0.0320 | 0.8076 | - |
408
+ | 8.8889 | 800 | 0.0038 | 0.0317 | 0.8095 | - |
409
+ | 10.0 | 900 | 0.0035 | 0.0318 | 0.8087 | 0.7661 |
410
+
411
+ * The bold row denotes the saved checkpoint.
412
+
413
+ ### Framework Versions
414
+ - Python: 3.10.12
415
+ - Sentence Transformers: 3.0.1
416
+ - Transformers: 4.42.4
417
+ - PyTorch: 2.3.1+cu121
418
+ - Accelerate: 0.32.1
419
+ - Datasets: 2.20.0
420
+ - Tokenizers: 0.19.1
421
+
422
+ ## Citation
423
+
424
+ ### BibTeX
425
+
426
+ #### Sentence Transformers
427
+ ```bibtex
428
+ @inproceedings{reimers-2019-sentence-bert,
429
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
430
+ author = "Reimers, Nils and Gurevych, Iryna",
431
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
432
+ month = "11",
433
+ year = "2019",
434
+ publisher = "Association for Computational Linguistics",
435
+ url = "https://arxiv.org/abs/1908.10084",
436
+ }
437
+ ```
438
+
439
+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
443
+ -->
444
+
445
+ <!--
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+ ## Model Card Authors
447
+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
449
+ -->
450
+
451
+ <!--
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+ ## Model Card Contact
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+
454
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
455
+ -->
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