---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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: 𑀟चन𑀙𑀢𑀟 𑀞च𑀪च𑀠च 𑀫𑁣प𑁣 𑀞न𑀠च 𑀞𑁣𑀱च ब𑀢𑀪𑀠च𑀯
  sentences:
  - '  णच ब𑀢𑀪𑀠च पच𑀪𑁦 𑀣च 𑀠च𑀫च𑀢𑀲𑀢णच𑀪𑀳च 𑀣च झच𑀟𑁦𑀟𑀳च ञचणच𑀦 𑀞च𑀠च𑀪 णच𑀣𑀣च 𑀠च𑀫च𑀢𑀲𑀢𑀟𑀳च णच ढच𑀪
    𑀢णचल𑀢𑀯'
  - ' 𑀣च𑀟बच𑀟𑁦 𑀣च 𑀟चन𑀙𑀢𑀟 𑀠𑁣पच𑀪𑀦 पच𑀟च 𑀢णच 𑀤च𑀠च ढचढढच 𑀞𑁣 𑀞च𑀪च𑀠च 𑀢𑀣च𑀟 च𑀞च 𑀞𑀱चपच𑀟पच 𑀣च
    𑀠𑁣पच𑀪 𑀣चन𑀞च𑀪 𑀫𑁣प𑁣 𑀣च 𑀳नख𑀦 𑀞न𑀠च णच 𑀲𑀢 𑀟च 𑀞𑁣𑀱च ब𑀢𑀪𑀠च𑀯'
  - पच𑀪𑁦𑀠𑀢 णच ढनबच 𑀱च झन𑀟ब𑀢णच𑀪 झ𑀱चलल𑁣𑀟 झच𑀲च पच ञचल𑀢ढ𑀢𑀟 झच𑀳च𑀪 𑀢𑀪च𑀟 च बच𑀳च𑀪 पन𑀪𑀞𑀢णणच
    𑀞न𑀠च णच त𑀢 𑀱च झन𑀟ब𑀢णच𑀪 𑀞𑀱चललचण𑁦 थ𑀯
- source_sentence: णच𑀟च बचढच 𑀣च लन𑀪च 𑀣च 𑀣च पच 𑀲𑀢 𑀣च
  sentences:
  - 𑀘𑁣𑀫𑀟 𑀠𑀢त𑀫च𑁦ल 𑁣ब𑀢𑀣𑀢 𑀝च𑀟 𑀫च𑀢𑀲𑁦𑀳𑀫𑀢 𑀪च𑀟च𑀪 𑀗 बच 𑀱चपच𑀟 𑀣𑀢𑀳च𑀠ढच𑀦 𑀭थ𑀖थ𑀮𑀯
  - ' 𑀱च𑀟𑀟च𑀟 णच𑀟च पच𑀢𑀠च𑀞च 𑀱च झ𑀱च𑀪च𑀪𑀪न𑀟 𑀫𑀪 𑀳न त𑀢 बचढच 𑀣च लन𑀪च 𑀣च 𑀣न𑀞 ढनञचञञ𑁦𑀟 चणणन𑀞च𑀟𑀳न
    𑀣च 𑀠च𑀳न 𑀟𑁦𑀠च पच 𑀫च𑀟णच𑀪 𑀣च पच 𑀲𑀢 𑀳चन𑀪𑀢 𑀣च 𑀳चनझ𑀢 𑀲𑀢ण𑁦 𑀣च 𑀣च𑀯'
  - ' च 𑀞च𑀪𑀞च𑀳𑀫𑀢𑀟 𑀣𑁣𑀞च𑀪𑀦 𑀠च𑀘चल𑀢𑀳च𑀪 लचनण𑁣ण𑀢𑀟 𑀢𑀟𑀣𑀢णच 𑀢पच त𑁦 ढचढढच𑀪 𑀫न𑀞न𑀠च𑀪 𑀞नलच 𑀣च 𑀫च𑀪𑀞𑁣𑀞𑀢𑀟
    𑀳𑀫च𑀪𑀢𑀙च च 𑀢𑀟𑀣𑀢णच 𑀣च 𑀞न𑀠च पचढढचपच𑀪 𑀣च ढ𑀢𑀟 𑀣𑁣𑀞च 𑀣च 𑀞𑀢णचण𑁦 𑀞च𑀙𑀢𑀣𑁣𑀘𑀢𑀟 𑀞𑀱च𑀪च𑀪𑀪न पच
    𑀫च𑀟णच𑀪 𑀞𑀱च𑀪च𑀪𑀪न𑀟 लचनणच च 𑀞च𑀳च𑀪𑀯'
- source_sentence: 𑀣नढच ढढत𑀕 𑀠च𑀠च𑀪 चलचप𑁣न𑀠𑀢
  sentences:
  - 𑀣नढच 𑀞न𑀠च  𑀣𑁦𑀟𑀞ष𑀣𑁦𑀟𑀞𑀠च𑀟च𑀤च𑀪पच  ढढत𑀕 𑀠च𑀠च𑀪 𑀞च𑀳𑀳𑁦ण चलचप𑁣न𑀠𑀢 𑀯
  - '  च𑀟 𑀲च𑀪च 𑀳च𑀠च𑀪𑀱च 𑀞न𑀠च 𑀣चबच ढचणच च𑀟 𑀲च𑀣च𑀣च चणणन𑀞च𑀟 बच 𑀳चन𑀪च𑀟 𑀢णचलच𑀢 𑀟च 𑀟च𑀘𑁦𑀪𑀢णच
    𑀠च𑀳न णच𑀪च𑀯'
  - '    𑀫च𑁥च𑀞च च𑀤चढपच𑀪𑀱च णच𑀟च 𑀣च 𑀱च𑀫चलच 𑀠न𑀳च𑀠𑀠च𑀟 च त𑀢𑀞𑀢𑀟 चणणन𑀞च𑀟 णचझ𑀢 𑀣च पच𑀱चबच𑀪𑀯'
- source_sentence: च𑀟
  sentences:
  - 𑀠नपन𑀱च च 𑀪च𑀟च𑀪 र बच 𑀱चपच𑀟 𑀠चणन𑀟 ठ𑀧𑀧ठ𑀦 च𑀞न 𑀟च त𑀢𑀞𑀢𑀟 𑀲च𑀳𑀢𑀟𑀘𑁣𑀘𑀢 𑀬𑀧 𑀣च 𑀞𑁦 त𑀢𑀞𑀢𑀟 𑀱च𑀟𑀢
    𑀘𑀢𑀪ब𑀢𑀟 𑀣च णच ण𑀢 𑀫चप𑀳च𑀪𑀢𑀟 𑀠𑀢𑀟पन𑀟च 𑀞चञच𑀟 ढचणच𑀟 पच𑀳𑀫𑀢𑀟𑀳च च 𑀞च𑀟𑁣𑀯
  - '  च𑀟 ण𑀢 𑀢𑀠च𑀟𑀢𑀟 𑀳𑀯'
  - ' 𑀲च𑀫च𑀣 णच 𑀞च𑀠𑀠चलच 𑀞च𑀞च𑀪 ठ𑀧𑀭ठट𑀭𑀰 𑀣च 𑀞𑀱चललचण𑁦 𑀭𑀧 𑀠च𑀳न ढच𑀟 𑀳𑀫च𑀙च𑀱च च 𑀱च𑀳च𑀟𑀟𑀢 ठ𑁢
    च 𑀣न𑀞 बच𑀳च𑀯'
- source_sentence: ब𑀫𑁣𑀳प 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀢𑀠च𑀞𑁣𑀟 𑀣च 𑀲च𑀳चलनललन𑀞च णच𑀟च ढच
    𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟
  sentences:
  - च𑀠𑀢𑀟पचतत𑀢णच च त𑀢𑀞𑀢𑀟 ब𑀫𑁣𑀳प 𑀳𑁦𑀪𑀢𑁦𑀳 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀪𑁦 𑀣च ञ𑀢𑀠ढ𑀢𑀟 𑀢𑀟बच𑀟पचपपन𑀟
    प𑀳च𑀪𑀢𑀟 पच𑀢𑀠च𑀞𑁣𑀟 𑀣𑀢𑀪𑁦ढच 𑀣च 𑀲च𑀳चलनललन𑀞च 𑀟च च𑀠𑀢𑀟त𑀢𑀦 णच𑀟च ढच 𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀞𑀱च𑀟त𑀢णच𑀪
    𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟 पच𑀲𑀢णच𑀪𑀳न𑀯
  - प𑁣ध𑀳ण ध𑀫𑀢𑀪𑀢 𑀝च𑀟 𑀫च𑀢𑀲𑁦 𑀳𑀫𑀢 च 𑀪च𑀟च𑀪 𑀭𑀭 बच 𑀱चपच𑀟 चबन𑀳पच 𑀭थ𑀗𑀧𑀮 ञच𑀟 𑀱च𑀳च𑀟 ढच𑀣𑀠𑀢𑀟प𑁣𑀟
    ञच𑀟 𑀤च𑀠ढ𑀢च 𑀟𑁦𑀯
  - पचबबच𑀲च𑀣𑀢 𑀠चप𑀳नबन𑀟𑀢𑀟 𑀠नपच𑀟𑁦 𑀟𑁦 च 𑀳च𑀳𑀫𑁦𑀟 च𑀪ल𑀢प 𑀣च𑀞𑁦 णच𑀟𑀞𑀢𑀟 चबच𑀣𑁦𑀤 च च𑀪𑁦𑀱च पच प𑀳च𑀞𑀢णच𑀪
    𑀟𑀢𑀘च𑀪𑀯
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **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_MiniLM_L12_2")
# Run inference
sentences = [
    'ब𑀫𑁣𑀳प 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀢𑀠च𑀞𑁣𑀟 𑀣च 𑀲च𑀳चलनललन𑀞च णच𑀟च ढच 𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟',
    'च𑀠𑀢𑀟पचतत𑀢णच च त𑀢𑀞𑀢𑀟 ब𑀫𑁣𑀳प 𑀳𑁦𑀪𑀢𑁦𑀳 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀪𑁦 𑀣च ञ𑀢𑀠ढ𑀢𑀟 𑀢𑀟बच𑀟पचपपन𑀟 प𑀳च𑀪𑀢𑀟 पच𑀢𑀠च𑀞𑁣𑀟 𑀣𑀢𑀪𑁦ढच 𑀣च 𑀲च𑀳चलनललन𑀞च 𑀟च च𑀠𑀢𑀟त𑀢𑀦 णच𑀟च ढच 𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀞𑀱च𑀟त𑀢णच𑀪 𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟 पच𑀲𑀢णच𑀪𑀳न𑀯',
    'प𑁣ध𑀳ण ध𑀫𑀢𑀪𑀢 𑀝च𑀟 𑀫च𑀢𑀲𑁦 𑀳𑀫𑀢 च 𑀪च𑀟च𑀪 𑀭𑀭 बच 𑀱चपच𑀟 चबन𑀳पच 𑀭थ𑀗𑀧𑀮 ञच𑀟 𑀱च𑀳च𑀟 ढच𑀣𑀠𑀢𑀟प𑁣𑀟 ञच𑀟 𑀤च𑀠ढ𑀢च 𑀟𑁦𑀯',
]
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)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## 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

#### 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: 4 tokens</li><li>mean: 37.72 tokens</li><li>max: 292 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 90.07 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence_0                                         | sentence_1                                                                                                                        |
  |:---------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
  | <code>𑀞न𑀣न ढ𑀢𑀪𑀟𑀢𑀟𑀦𑀞न𑀳च प𑁦𑀞न𑀟</code>                | <code>प𑁦𑀞न𑀟 पचबच णच𑀟च 𑀞न𑀣न 𑀣च ढ𑀢𑀪𑀟𑀢𑀟𑀦𑀞न𑀳च 𑀣च प𑁦𑀞न𑀟 पचत𑀫𑁣बच𑀯</code>                                                                |
  | <code>च त𑀢ढ𑀢ण𑁣ण𑀢𑀟 𑀳च𑀣च𑀪𑀱च𑀪 𑀳न झच𑀪च 𑀠चप𑀳चण𑀢𑀟</code> | <code>चढ𑁣𑀞च𑀢𑀞च𑀠च𑀪 च णच𑀱च𑀟त𑀢𑀟 त𑀢ढ𑀢ण𑁣ण𑀢𑀟 𑀳च𑀣च𑀪𑀱च𑀪 𑀘च𑀠च𑀙च𑀦 𑀠च𑀳न च𑀠𑀲च𑀟𑀢 𑀤च 𑀳न 𑀢णच झच𑀪च 𑀠नपच𑀟𑁦 च 𑀠चप𑀳चण𑀢𑀟 चढ𑁣𑀞च𑀟𑀳न𑀯</code>             |
  | <code>𑀣च बन𑀣न𑀠𑀠च𑀱च 𑀘च𑀪𑀢𑀣न𑀟 𑀠न𑀘चललन पच 𑀯</code>     | <code>   पच ढच 𑀣च बन𑀣न𑀠𑀠च𑀱च बच 𑀘च𑀪𑀢𑀣न𑀟 च𑀟च𑀪त𑀫𑀢𑀳प 𑀣चढच𑀟ष𑀣चढच𑀟 𑀣च 𑀠न𑀘चललन 𑀠च𑀳न चलचझच 𑀣च झन𑀟ब𑀢णच𑀪 𑀠च𑀙च𑀢𑀞चपच 𑀙णच𑀟त𑀢 पच 𑀘च𑀠न𑀳 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   | 2.5392        |
| 0.25  | 1000  | 1.4129        |
| 0.375 | 1500  | 1.3383        |
| 0.5   | 2000  | 1.288         |
| 0.625 | 2500  | 1.2627        |
| 0.75  | 3000  | 1.239         |
| 0.875 | 3500  | 1.2208        |
| 1.0   | 4000  | 1.2041        |
| 1.125 | 4500  | 1.1743        |
| 1.25  | 5000  | 1.1633        |
| 1.375 | 5500  | 1.1526        |
| 1.5   | 6000  | 1.1375        |
| 1.625 | 6500  | 1.1313        |
| 1.75  | 7000  | 1.1246        |
| 1.875 | 7500  | 1.1162        |
| 2.0   | 8000  | 1.1096        |
| 2.125 | 8500  | 1.0876        |
| 2.25  | 9000  | 1.0839        |
| 2.375 | 9500  | 1.0791        |
| 2.5   | 10000 | 1.0697        |
| 2.625 | 10500 | 1.0671        |
| 2.75  | 11000 | 1.0644        |
| 2.875 | 11500 | 1.0579        |
| 3.0   | 12000 | 1.0528        |


### 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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