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--- |
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base_model: distilbert/distilbert-base-multilingual-cased |
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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:654495 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: সম্পূৰ্ণৰূপে ভিন্ন ধৰণৰ পেৰাচুট আৰু এটা উড়ন্ত পক্ষীৰ মাজত, আহ্, |
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শব্দৰ তিনিগুণ বেগত, ঘণ্টাৰ ২২, ০০০ মাইলত। |
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sentences: |
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- ঘণ্টাৰ ২০, ০০০ কিলোমিটাৰতকৈ অধিক গতিত উড়ে। |
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- মোৰ ঘৰত দুটা কম্পিউটাৰ আছে। |
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- সকলো ক্ৰীড়াৰ নাম ক্ৰীড়াত ব্যৱহাৰ কৰা এটা সঁজুলিৰ নামেৰে নামকৰণ কৰা হয়। |
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- source_sentence: আৰু তাৰ পিছত মই তেওঁক যাবলৈ শুনিছিলোঁ, সেয়েহে মই এতিয়াও মোৰ কাম |
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শেষ কৰি আছো। |
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sentences: |
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- মই আজি যিটো কৰিব লাগিব সেয়া কৰি আছো। |
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- '"Bato (বা" "vato" ") এটা স্পেনিছ শব্দ যাৰ অৰ্থ হৈছে" "পুৰুষ" "বা" "বন্ধু" "।"' |
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- পিতৃ-মাতৃয়ে ঘৰত থাকিল। |
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- source_sentence: মই কেৱল বুজাবলৈ চেষ্টা কৰিছিলোঁ। |
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sentences: |
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- মই বুজিবলৈ চেষ্টা কৰিছিলোঁ। |
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- মই আন কেইবাটাও প্ৰস্তাৱ দিবলৈ আহিছিলোঁ। |
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- প্ৰেমিক নামৰ এজন খেতিয়কে নিজৰ হত্যাৰ আঁচনি তৈয়াৰ কৰোতে ঘাসপূৰ্ণ স্থানত লুকুৱাই |
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থৈ যায়। |
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- source_sentence: আৰু, উম, যদি এইটো বাঢ়ি আহিব আৰু কেৱল বাঢ়ি আহিব তেতিয়াহ 'লে' |
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whish 'হ' ব, আৰু যেনেকৈ ই আপোনাৰ মূৰটো বন্ধ কৰি দিব। |
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sentences: |
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- প্ৰাৰম্ভিক শিক্ষা লাভ কৰা আৰু বয়সস্থ ল 'ৰা-ছোৱালীয়ে প্ৰায়ে ভৱিষ্যতৰ বিষয়ে |
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সপোন দেখে। |
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- তেওঁলোকে মোৰ ওচৰলৈ কিয় আহিছে বুলি প্ৰশ্ন কৰিলে। |
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- যদি কোনো ধৰণৰ পৰিৱৰ্তন হয়, তেনেহ 'লে তাৰ লগত এক শব্দ বাঢ়িব পাৰে। |
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- source_sentence: মই ভালদৰে জানিব নোৱাৰোঁ আপোনালোকৰ সৈতে কথা বতৰা আৰু এক ভাল সন্ধ্যা |
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আছিল |
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sentences: |
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- মই নিশ্চিত নহয় কিন্তু মই অলপ ভাল, আজি ৰাতি আপোনালোকৰ সৈতে কথা পাতিবলৈ পাই ভাল |
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লাগিল। |
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- Shannon এ বাৰ্তা উপেক্ষা কৰিছে। |
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- মানুহজনে ষ্টক এক্সচেঞ্জত লেনদেনৰ বিষয়ে জানিবলৈ চেষ্টা কৰিছিল। |
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model-index: |
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- name: SentenceTransformer based on distilbert/distilbert-base-multilingual-cased |
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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: 0.7169579983340281 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.7220987460972806 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.7380110422340219 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.7452082040848071 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.7386577662108481 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.7458961406429292 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.6480820840127198 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.6478256799308721 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.7386577662108481 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.7458961406429292 |
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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: 0.656822131496386 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6621886312595516 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.6675496858061083 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.6722470705036974 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.6681862838868354 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.6727345795749732 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.5691955650489428 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.570867962692759 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.6681862838868354 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.6727345795749732 |
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name: Spearman Max |
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--- |
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|
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# SentenceTransformer based on distilbert/distilbert-base-multilingual-cased |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased). 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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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e --> |
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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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### 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': 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}) |
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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("pritamdeka/distilbert-base-multilingual-cased-indicxnli-random-negatives-v1") |
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# Run inference |
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sentences = [ |
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'মই ভালদৰে জানিব নোৱাৰোঁ আপোনালোকৰ সৈতে কথা বতৰা আৰু এক ভাল সন্ধ্যা আছিল', |
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'মই নিশ্চিত নহয় কিন্তু মই অলপ ভাল, আজি ৰাতি আপোনালোকৰ সৈতে কথা পাতিবলৈ পাই ভাল লাগিল।', |
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'Shannon এ বাৰ্তা উপেক্ষা কৰিছে।', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### 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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--> |
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<!-- |
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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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</details> |
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--> |
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<!-- |
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### 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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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* 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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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.717 | |
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| **spearman_cosine** | **0.7221** | |
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| pearson_manhattan | 0.738 | |
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| spearman_manhattan | 0.7452 | |
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| pearson_euclidean | 0.7387 | |
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| spearman_euclidean | 0.7459 | |
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| pearson_dot | 0.6481 | |
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| spearman_dot | 0.6478 | |
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| pearson_max | 0.7387 | |
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| spearman_max | 0.7459 | |
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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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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.6568 | |
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| **spearman_cosine** | **0.6622** | |
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| pearson_manhattan | 0.6675 | |
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| spearman_manhattan | 0.6722 | |
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| pearson_euclidean | 0.6682 | |
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| spearman_euclidean | 0.6727 | |
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| pearson_dot | 0.5692 | |
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| spearman_dot | 0.5709 | |
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| pearson_max | 0.6682 | |
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| spearman_max | 0.6727 | |
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<!-- |
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## Bias, Risks and Limitations |
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*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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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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`: 1 |
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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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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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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`: 1 |
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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 |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | loss | pritamdeka/stsb-assamese-translated-dev_spearman_cosine | pritamdeka/stsb-assamese-translated-test_spearman_cosine | |
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|:----------:|:---------:|:-------------:|:----------:|:-------------------------------------------------------:|:--------------------------------------------------------:| |
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| 0 | 0 | - | - | 0.5489 | - | |
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| 0.0489 | 500 | 1.9387 | 1.7308 | 0.6808 | - | |
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| 0.0978 | 1000 | 1.0503 | 1.7373 | 0.6689 | - | |
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| 0.1467 | 1500 | 0.92 | 1.5838 | 0.6761 | - | |
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| 0.1956 | 2000 | 0.8754 | 1.4807 | 0.6518 | - | |
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| 0.2445 | 2500 | 0.7988 | 1.3797 | 0.6853 | - | |
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| 0.2933 | 3000 | 0.7606 | 1.3713 | 0.7108 | - | |
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| 0.3422 | 3500 | 0.7228 | 1.2510 | 0.6677 | - | |
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| 0.3911 | 4000 | 0.688 | 1.2374 | 0.6734 | - | |
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| 0.4400 | 4500 | 0.6992 | 1.2173 | 0.6891 | - | |
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| 0.4889 | 5000 | 0.6108 | 1.1638 | 0.7017 | - | |
|
| 0.5378 | 5500 | 0.612 | 1.0815 | 0.7102 | - | |
|
| 0.5867 | 6000 | 0.6259 | 1.0664 | 0.7202 | - | |
|
| 0.6356 | 6500 | 0.5863 | 1.0464 | 0.7047 | - | |
|
| 0.6845 | 7000 | 0.5941 | 1.0111 | 0.7101 | - | |
|
| 0.7334 | 7500 | 0.5436 | 1.0023 | 0.7171 | - | |
|
| 0.7822 | 8000 | 0.555 | 0.9633 | 0.7202 | - | |
|
| 0.8311 | 8500 | 0.5466 | 0.9651 | 0.7279 | - | |
|
| 0.8800 | 9000 | 0.5326 | 0.9611 | 0.7262 | - | |
|
| 0.9289 | 9500 | 0.5055 | 0.9313 | 0.7276 | - | |
|
| **0.9778** | **10000** | **0.4828** | **0.9172** | **0.7221** | **-** | |
|
| 1.0 | 10227 | - | - | - | 0.6622 | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.3.1+cu121 |
|
- Accelerate: 0.32.1 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
|
|
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### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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