README.md

---
base_model: shibing624/text2vec-base-multilingual
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:64000
- loss:DenoisingAutoEncoderLoss
widget:
- source_sentence: च बच 𑀱चपच𑀟 पच पच 𑀙णच𑀪 𑀱च𑀳च 𑀠च𑀢 𑀳𑀫𑁦𑀞च𑀪न𑀣च पच 𑀞𑀱चलल𑁣 पच𑀪𑀢𑀫𑀢𑀟 ल𑁣𑀞चत𑀢𑀟
    𑀱च𑀳च𑀟 𑀳च𑀠न 𑀟च𑀳च𑀪च 𑀱च𑀟𑀣च च ल𑁦खच𑀟प𑁦 लच𑀳 धलच𑀟च𑀳 𑀣𑀢ख𑀢𑀳𑀢𑀨𑀟
  sentences:
  - ' च𑀟 पच𑀟पच𑀟त𑁦 पच च 𑀠चप𑀳चण𑀢𑀟 गणच𑀪 पच𑀞च𑀪च𑀪 𑁦च𑀳पल𑁦𑀢ब𑀫 च 𑀤चढ𑁦𑀟 𑀲𑀢𑀣𑀣च ब𑀱च𑀟𑀢 𑀟च 𑀳𑀫𑁦𑀞च𑀪च𑀪
    𑀭थथर च𑀠𑀠च पच 𑀳𑀫च 𑀞चण𑁦 च 𑀤चढ𑁦𑀟𑀯'
  - ' च 𑀪च𑀟च𑀪 ठ𑀖 बच 𑀱चपच𑀟 𑀘च𑀟च𑀢𑀪न च 𑀳𑀫𑁦𑀞च𑀪च𑀪 ठ𑀧ठ𑀰 पच 𑀞च𑀲च पच𑀪𑀢𑀫𑀢 पच 𑀤च𑀠च 𑀠चपच𑀳𑀫𑀢णच𑀪
    𑀙णच𑀪 𑀱च𑀳च 𑀠च𑀢 𑀞च𑀪च𑀟त𑀢𑀟 𑀳𑀫𑁦𑀞च𑀪न𑀣च पच त𑀢 𑀞𑀱चलल𑁣 च पच𑀪𑀢𑀫𑀢𑀟 ढच𑀪तच ल𑁣𑀞चत𑀢𑀟 𑀣च पच त𑀢
    च 𑀱च𑀳च𑀟 𑀣च 𑀳न𑀞च 𑀳च𑀠न 𑀟च𑀳च𑀪च 𑀱च𑀟𑀣च 𑀞न𑀟ब𑀢णच𑀪 पच ढच𑀪त𑁦ल𑁣𑀟च 𑀬ष𑀧 च 𑀞च𑀟 ल𑁦खच𑀟प𑁦 लच𑀳
    धलच𑀟च𑀳 च 𑀱च𑀳च𑀟 ध𑀪𑀢𑀠𑁦𑀪च 𑀣𑀢ख𑀢𑀳𑀢𑀨𑀟 𑀯'
  - '  च 𑀞च𑀞च𑀪 𑀱च𑀳च𑀟𑀳च 𑀟च ढ𑀢णन च त𑀢𑀞𑀢𑀟 ठ𑀧ठ𑀭𑀦 णच 𑀤च𑀠च 𑀣च𑀟 𑀱च𑀳च च 𑀞नल𑁣ढ 𑀣𑀢𑀟 𑀞न𑀠च णच
    पच𑀢𑀠च𑀞च 𑀠न𑀳न 𑀳न𑀟 त𑀢 𑁦पपच𑀟 ठ𑀧ठ𑀭𑀦 𑀞न𑀠च च𑀟 𑀟च𑀣च 𑀳𑀫𑀢 ब𑀱च𑀟𑀢𑀟 बच𑀳च𑀪 𑀞च𑀞च𑀪 𑀱च𑀳च𑀯'
- source_sentence: 𑀣च 𑀟च प𑀳𑁦𑀪𑁦𑀟 च
  sentences:
  - ल𑀢𑀳𑀳च𑀲𑀢𑀟 ल𑀢𑀳𑀳च𑀲𑀢𑀟 𑀫चझझ𑀢𑀟 𑀫चझझ𑀢𑀟 𑀠चललच𑀞चबचढचञचणचपच𑀞च𑀣𑀣न𑀟 𑀣च च𑀞च ण𑀢 𑀟𑀢णणच 𑀟च 𑀠न𑀳च𑀠𑀠च𑀟  𑀣𑁣𑀞च𑀪
    𑀫चझझ𑀢𑀟 𑀠चढन𑀞चत𑀢 𑀣𑁣𑀞च𑀪 𑀫च𑀞𑀞𑁣𑀞𑀢𑀟 𑀠च𑀪च ब𑀢𑀣च 𑀣𑁣𑀞च𑀪 𑀫चझझ𑀢𑀟 𑀠च𑀢 ढ𑀢णच𑀟 𑀫च𑀪च𑀘𑀢 𑀣𑁣𑀞च𑀪 𑀫च𑀞𑀞𑁣𑀞𑀢𑀟
    𑀢ल𑀢𑀠𑀢 𑀦 𑀣𑁣𑀞च𑀪 𑀫च𑀞𑀞𑁣𑀞𑀢𑀟 प𑀳𑁣𑀫𑁣𑀟 𑀳𑁣𑀘𑁣𑀘𑀢 ब𑀢 ढ𑀢लल 𑁣𑀲 𑀪𑀢ब𑀫प𑀳𑀦 𑀱च𑀟𑀣च च𑀞च 𑀲𑀢 𑀳च𑀟𑀢 𑀣च ब𑀢
    ढ𑀢लल 𑀣𑁣𑀞च𑀪 𑀙णच𑀟 लन𑀱च𑀣𑀢𑀦 पच𑀪𑁣𑀟 झन𑀟ब𑀢ण𑁣ण𑀢𑀟 𑀙णच𑀟 लन𑀱च𑀣𑀢 𑀟च च𑀪𑁦𑀱चत𑀢𑀟 च𑀠𑀢𑀪𑀞च 𑀟𑁦 𑀳न𑀞च
    प𑀳च𑀪च 𑀣𑁣𑀞च𑀪 𑀫चझझ𑀢𑀟 लचढन𑀪च𑀪𑁦𑀦 झन𑀟ब𑀢णच𑀪 लचढन𑀪च𑀪𑁦 पच च𑀠𑀢𑀪𑀞च पच ढनबच 𑀣𑁣𑀞च𑀪 𑀫चझझ𑀢𑀟
    𑀠न𑀫चलल𑀢 𑀞𑁣 च𑀘च𑀟𑀣च ठ𑀭 𑀞न𑀣𑀢𑀪𑀢𑀟 𑀫च𑀞𑀞𑀢 𑀟च 𑀠च𑀫चल𑀢तत𑀢𑀦 𑀠च𑀪नढनपच𑀟 ढच𑀟 𑀣च𑀪𑀢णच 𑀣च 𑀠च𑀳न
    𑀲च𑀳च𑀫च 𑀣𑁣𑀞च𑀪 𑀫चझझ𑀢𑀟 𑀠च𑀢 ढच 𑀣च बन𑀣न𑀠𑀠च𑀱च𑀦 𑀣𑁣𑀟 𑀠च𑀳न ढच 𑀣च चबच𑀘𑀢 𑀞न𑀣𑀢𑀪𑀢𑀟 𑀫च𑀞𑀞𑁣𑀞𑀢𑀟
    𑀘च𑀠𑀢𑀙च𑀟 𑀣𑁣𑀞च 𑀣𑁣𑀞च𑀪 𑀫चझझ𑀢𑀟 𑀠च𑀳न 𑀤च𑁥𑁦 पच तचल𑀢𑀲𑁣𑀪𑀟𑀢च𑀦 𑀣च𑀢𑀣च𑀢पच𑀱च 𑀣च 𑀣𑁣𑀞च𑀪 𑀫चझझ𑀢𑀟
    𑀤च𑁥𑁦 𑀣𑁣𑀞च𑀪 𑀠न𑀳नलन𑀟त𑀢 पच 𑀫च𑀞𑀞𑁣𑀞𑀢𑀟 𑀠चपच च 𑀠च𑀳चललचत𑀢𑀟 𑀟𑁦𑀱 𑀘𑁦𑀪𑀳𑁦ण 𑀣𑁣𑀞च𑀪 𑀫चझझ𑀢𑀟 𑀫चझझ𑀢𑀟
    त𑀢𑀟 𑀫च𑀟त𑀢 𑀣च 𑀪च𑀳𑀫च𑀱च 𑀞न𑀣𑀢𑀪𑀢𑀟 𑀫चझझ𑀢𑀟 𑀠च𑀳न 𑀞चप𑀢𑀟 𑀞𑀢𑀪𑁦𑀣𑀢प𑀦 𑀱च𑀟𑀣च 𑀞𑁦 झन𑀟𑀳𑀫𑁦 च त𑀢𑀞𑀢𑀟
    𑀣𑁣𑀞च𑀪 तच𑀪𑀣 𑀟च 𑀳𑀫𑁦𑀞च𑀪चपच ठ𑀧𑀧थ 𑀣𑁣𑀞𑁣𑀞𑀢𑀟 𑀫चझझ𑀢𑀟 𑀠च𑀳न त𑀢 बचढच𑀟 त𑀢𑀟 𑀣न𑀪𑀢 𑀣च 𑀘𑀢𑀠च𑀙𑀢 𑀝𑀣𑁣𑀞च𑀪
    𑀫चझझ𑀢𑀟 𑀠च𑀳न त𑀢 बचढच 𑀣च 𑀘𑀢𑀠च𑀙𑀢 𑀮𑀣नढच 𑀱च𑀳न चढनढन𑀱च𑀟  झ𑀢𑀪च𑀪 𑀫चझझ𑀢𑀟 ढ𑀢𑀪𑀢पच𑀟𑀢णच 𑀫चझझ𑀢𑀟
    𑀣च ढच 𑀤च च 𑀢णच पचनण𑁦𑀱च ढच 𑀣𑁣𑀞च𑀪 𑀞च𑀪𑁦 𑀫च𑀞𑀞𑁣𑀞𑀢𑀟 𑀣च𑀟 च𑀣च𑀠 पच 𑀣न𑀟𑀢णच 𑀞च𑀙𑀢𑀣𑁣𑀘𑀢𑀟 𑀞च𑀪𑁦
    𑀫च𑀞𑀞𑀢𑀟 ढ𑀢ल𑀙च𑀣च𑀠च 𑀟च 𑀣न𑀟𑀢णच 𑀫च𑀞𑀞𑁣𑀞𑀢𑀟 𑀫चल𑀢पपच प𑀳च𑀪𑀢𑀟 𑀣𑁣𑀞च 𑀣𑁣𑀞च𑀪 𑀫च𑀞𑀞𑀢 𑀟च ढ𑀢णन𑀠च𑀟च𑀤च𑀪पच𑀯
  - 𑀭𑀰𑀮𑀦 𑀣च लच𑀠ढच𑀪 पचबनललच च त𑀢𑀞𑀢𑀟 𑀪न𑀞न𑀟𑀢𑀟 ढठ 𑀟च प𑀳𑁦𑀪𑁦𑀟 झच𑀳च च 𑀧𑀕𑀖र𑀯
  - द द द य𑀞न𑀠च 𑀞न ढचनपच 𑀱च चललच𑀫 𑀞न𑀠च 𑀞च 𑀣च 𑀞न 𑀫चञच𑀱च𑀟𑀢 𑀣च 𑀳𑀫𑀢द 𑀞न𑀠च बच 𑀠च𑀫च𑀢𑀲च 𑀞न
    ण𑀢 𑀞णचनपचपच𑀱च𑀦 𑀞न𑀠च बच 𑀠चभ चढ𑁣पच 𑀤न𑀠न𑀟पच 𑀣च 𑀠च𑀪चणन 𑀣च 𑀠चपचलचनपच 𑀣च 𑀠चझ𑀱चढत𑀢 𑀠चभचढनत𑀢𑀟
    𑀞न𑀳च𑀟पच𑀦 𑀣च 𑀠चझ𑀱चढत𑀢 𑀠च𑀟𑀢𑀳च𑀟त𑀢𑀦 𑀣च चढ𑁣𑀞𑀢च ब𑁦𑀲𑁦 𑀣च 𑀩च𑀟 𑀫च𑀟णच 𑀣च चढ𑀢𑀟 𑀣च 𑀫च𑀟𑀟न𑀱च𑀟𑀞न
    𑀟च 𑀣च𑀠च 𑀳न𑀞च 𑀠चललच𑀞च𑀯
- source_sentence: पच𑀞च 𑀪च𑀱च𑀪 च 𑀳च𑀪𑀞𑀢
  sentences:
  - ' णच पच𑀞च 𑀪च𑀱च𑀪 बच𑀟𑀢 च 𑀠चप𑀳चण𑀢𑀟𑀦 𑀳च𑀪𑀞𑀢 𑀣च𑀠ढच च त𑀢𑀞𑀢𑀟 𑀳𑀫𑀢𑀪𑀢𑀟𑀯'
  - थ𑀰𑀭𑀗𑀖ठ𑀰ठ𑁢थ𑁢𑀭      𑀦             𑀭𑀧𑀯
  - पचलचढ𑀢𑀘च𑀟 𑀣च 𑀪𑁦𑀣𑀢ण𑁣 च𑀟 बचढचपच𑀪 𑀣च पचलचढ𑀢𑀘𑀢𑀟 बच ब𑀫च𑀟च च 𑀭थ𑁢𑀖 𑀞न𑀠च णच𑀟च 𑀞च𑀪𑀞च𑀳𑀫𑀢𑀟
    𑀢𑀞𑁣𑀟 𑀘𑀢𑀫च𑀯
- source_sentence: 𑀱चप𑀳च 𑀣च 𑀟च𑀣च 𑀳न𑀦 𑀣𑀪𑀢ख𑁦 𑀞𑁣𑀱च𑀟𑁦 णच 𑀣च च ल𑁣𑀞चत𑀢𑀟 𑀫च𑀳च𑀳𑀫𑁦𑀟𑀳च𑀯
  sentences:
  - ' ण𑀢𑀟 च𑀢𑀞𑀢 पच𑀪𑁦 𑀣च 𑀳चन𑀪च𑀟 𑀞न𑀟ब𑀢ण𑁣ण𑀢𑀟 णच𑀫न𑀣च𑀱च 𑀣𑁣𑀟 𑀞च𑀪च 𑀱चणच𑀪 𑀣च 𑀞च𑀟 णच𑀟 च𑀣च𑀠 𑀣च
    𑀞च𑀪𑀲च𑀲च 𑀫𑀢𑀠𑀠च च प𑀳च𑀞च𑀟𑀢𑀟 चल𑀙न𑀠𑀠𑁣𑀠𑀢𑀟 णच𑀫न𑀣च𑀱च च 𑀠च𑀣च𑀣𑀢𑀟 𑀱च𑀣च𑀟𑀣च च𑀞च 𑀲चपचपपच𑀞च 𑀣च
    𑀱च𑀣च𑀟𑀣च च𑀞𑁦 𑀤चलन𑀟पच च 𑀣न𑀟𑀢णच𑀯'
  - ' 𑀫𑁣पन𑀟च𑀟 च𑀟च 𑀱चप𑀳च 𑀳न पच 𑀫च𑀟णच𑀪 𑀟च𑀙न𑀪च𑀪 𑀣चन𑀞च𑀪 𑀫𑁣प𑁣 𑀣च𑀢𑀣च𑀢 𑀣च णच𑀣𑀣च च𑀞च 𑀟च𑀣च
    𑀳न𑀦 पच𑀪𑁦 𑀣च  ब𑁦𑀟𑁦खच 𑀣𑀪𑀢ख𑁦 𑀣च 𑀞𑁦 पचढढचपच𑀪 𑀣च त𑁦𑀱च 𑀞𑁣𑀱च𑀟𑁦 𑀲𑀢𑀪च𑀠 णच त𑀢 बचढच 𑀣च 𑀞च𑀳च𑀟त𑁦𑀱च
    च त𑀢𑀞𑀢𑀟 बच𑀘𑁦𑀪𑁦𑀟 ल𑁣𑀞चत𑀢𑀟 𑀫च𑀳च𑀳𑀫𑁦𑀟𑀳च𑀯'
  - ' 𑀪च𑀠न𑀞च च त𑀢𑀞𑀢𑀟 झच𑀟च𑀟च𑀟 𑀪चढ𑁣 𑀣𑁣𑀟 𑀞च𑀪𑁦 प𑀳𑀢𑀪𑁦षप𑀳𑀢𑀪𑁦 𑀣चबच 𑀲𑁦𑀳च 𑀠चबच𑀟𑀢𑀟 𑀫𑁦𑀪ढ𑀢त𑀢𑀣𑁦𑀳
    𑀣च लचलचपच 𑀪𑁣𑀣𑁦𑀟प𑀯'
- source_sentence: 𑀠चपच𑀞𑀢𑀟 पच𑀟च ढनबच 𑀱च 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀱च𑀳च𑀯
  sentences:
  - ' च 𑀠चपच𑀞𑀢𑀟 𑀞नल𑁣ढ पच𑀟च ढनबच 𑀱च 𑀞𑁣𑀠च𑀳 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀟च𑀠𑀢ढ𑀢च 𑀱च𑀳च𑀯'
  - ' णच𑀟𑀞न𑀟च𑀟 बन𑀟𑀣न𑀠च𑀪 𑀘𑀣𑁦ण𑀣𑁦𑀫 ब𑀢𑀣च 𑀟𑁦 बच ब𑀢𑀣च𑀘𑁦 𑀠च𑀳न णच𑀱च 𑀟च झच𑀪𑀟𑀢 𑀟च 𑀭𑁢 𑀣च 𑀟च 𑀭𑀬
    𑀟च चल𑁦धध𑀢𑀟 ढ𑁣न𑀪ब𑁦𑁣𑀢𑀳𑀢𑁦𑀦 𑀱चञच𑀟𑀣च 𑀞𑁦 ञचन𑀞𑁦 𑀣च 𑀤च𑀟𑁦𑀟 𑀣नप𑀳𑁦𑀯'
  - 𑀪च𑀪𑀪चढच 𑀳𑀫𑁦𑀞च𑀪न𑀟 णच 𑀞च𑀳च𑀟त𑁦 ठर𑀯
---

# SentenceTransformer based on shibing624/text2vec-base-multilingual

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) <!-- at revision e9215a523d4324733a3c8279d0adff7bf37a7a77 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("T-Blue/tsdae_pro_text2vec")
# Run inference
sentences = [
    '𑀠चपच𑀞𑀢𑀟 पच𑀟च ढनबच 𑀱च 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀱च𑀳च𑀯',
    ' च 𑀠चपच𑀞𑀢𑀟 𑀞नल𑁣ढ पच𑀟च ढनबच 𑀱च 𑀞𑁣𑀠च𑀳 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀟च𑀠𑀢ढ𑀢च 𑀱च𑀳च𑀯',
    ' णच𑀟𑀞न𑀟च𑀟 बन𑀟𑀣न𑀠च𑀪 𑀘𑀣𑁦ण𑀣𑁦𑀫 ब𑀢𑀣च 𑀟𑁦 बच ब𑀢𑀣च𑀘𑁦 𑀠च𑀳न णच𑀱च 𑀟च झच𑀪𑀟𑀢 𑀟च 𑀭𑁢 𑀣च 𑀟च 𑀭𑀬 𑀟च चल𑁦धध𑀢𑀟 ढ𑁣न𑀪ब𑁦𑁣𑀢𑀳𑀢𑁦𑀦 𑀱चञच𑀟𑀣च 𑀞𑁦 ञचन𑀞𑁦 𑀣च 𑀤च𑀟𑁦𑀟 𑀣नप𑀳𑁦𑀯',
]
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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 64,000 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 37.42 tokens</li><li>max: 342 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 89.84 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence_0                                           | sentence_1                                                                                                                  |
  |:-----------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | <code>𑀠नपच𑀟𑁦𑀫च𑀢𑀫न𑀱च𑀟 𑀭थथ𑀬𑀯</code>                    | <code>𑀞𑀢𑀣𑀢𑀣𑀣𑀢बच𑀪 𑀳च𑀟च𑀙च𑀞नल𑁣ढझच𑀳च𑀳𑀫𑁦𑀟 𑀣न𑀟𑀢णच𑀠च𑀟च𑀤च𑀪पच 𑀪चणचणणन𑀟 𑀠नपच𑀟𑁦𑀫च𑀢𑀫न𑀱च𑀟 𑀭थथ𑀬𑀯</code>                                   |
  | <code>च 𑀱च𑀘𑁦𑀟 𑀘च𑀠भ𑀢णणच 𑀠च𑀢 𑀞𑀢𑀳𑀫𑀢𑀟 पच बच𑀳𑀞𑀢णच𑀯</code> | <code>𑀘च𑀠भ𑀢णणच𑀪 च ल𑁣𑀞चत𑀢𑀟 𑀢पच त𑁦 पच ढ𑀢णन 𑀣च पच ण𑀢 𑀟च𑀠𑀢𑀘𑀢𑀟 𑀞𑁣𑀞च𑀪𑀢 𑀱च𑀘𑁦𑀟 𑀳च𑀠च𑀪 𑀣च 𑀘च𑀠भ𑀢णणच 𑀠च𑀢 𑀞𑀢𑀳𑀫𑀢𑀟 𑀞च𑀳च पच बच𑀳𑀞𑀢णच𑀯</code> |
  | <code>𑀯</code>                                       | <code>𑀯</code>                                                                                                              |
* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step  | Training Loss |
|:-----:|:-----:|:-------------:|
| 0.125 | 500   | 4.0592        |
| 0.25  | 1000  | 1.6454        |
| 0.375 | 1500  | 1.4774        |
| 0.5   | 2000  | 1.4131        |
| 0.625 | 2500  | 1.3766        |
| 0.75  | 3000  | 1.3488        |
| 0.875 | 3500  | 1.3252        |
| 1.0   | 4000  | 1.3087        |
| 1.125 | 4500  | 1.2931        |
| 1.25  | 5000  | 1.2772        |
| 1.375 | 5500  | 1.2655        |
| 1.5   | 6000  | 1.2535        |
| 1.625 | 6500  | 1.243         |
| 1.75  | 7000  | 1.2305        |
| 1.875 | 7500  | 1.223         |
| 2.0   | 8000  | 1.216         |
| 2.125 | 8500  | 1.2073        |
| 2.25  | 9000  | 1.1999        |
| 2.375 | 9500  | 1.1935        |
| 2.5   | 10000 | 1.1872        |
| 2.625 | 10500 | 1.1804        |
| 2.75  | 11000 | 1.17          |
| 2.875 | 11500 | 1.167         |
| 3.0   | 12000 | 1.1623        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.33.0
- Datasets: 2.18.0
- Tokenizers: 0.19.1

## 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",
}
```

#### DenoisingAutoEncoderLoss
```bibtex
@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", 
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}
```

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