---
base_model: google-bert/bert-base-uncased
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:97043
- loss:DenoisingAutoEncoderLoss
widget:
- source_sentence: ढचणच𑀟च𑀟
  sentences:
  - ढ𑀢ढल𑀢𑁣ब𑀪चध𑀫ण ढचणच𑀟च𑀟 𑀞नलच𑀠च𑀟च𑀤च𑀪पच𑀯
  - ' णच 𑀪𑀢𑀞𑁦 𑀱च𑀟𑀟च𑀟 𑀠न𑀞च𑀠𑀢𑀟 𑀫च𑀪 𑀤न𑀱च 𑀭थ𑁢𑀰𑀯'
  - ' च त𑀢𑀞𑀢𑀟 𑀠च𑀘चल𑀢𑀳च𑀪𑀠च𑀟च𑀤च𑀪पच𑀯'
- source_sentence: त𑁣𑀠
  sentences:
  - ' 𑀲𑀪𑁦𑁦𑀣𑁣𑀠 𑀫𑁣न𑀳𑁦 पच ढच𑀢𑀱च 𑀳न𑀣च𑀟 𑀠चप𑀳चण𑀢 𑀠च𑀲𑀢 झच𑀳झच𑀟त𑀢 च प𑀳च𑀞च𑀟𑀢𑀟 ब𑀱च𑀠𑀟चप𑁣त𑀢𑀟 𑀣च𑀟𑀟𑀢णच
    च 𑀳𑀫𑁦𑀞च𑀪च पच ठ𑀧𑀭ठ𑀯'
  - 𑀖𑀖फ𑀮𑀦 𑁣𑀪𑁣𑀠𑁣 𑀝ठ𑀗𑀯
  - त𑁣𑀠 𑀯
- source_sentence: 𑀣च𑀟णच𑀟 𑀝𑀭थथ𑀬षठ𑀧𑀧ठ𑀮
  sentences:
  - ' 𑀣च𑀟णच𑀟 𑀝𑀭थथ𑀬षठ𑀧𑀧ठ𑀮 ध𑀪𑁣𑀲𑀯'
  - 𑀳त𑀯
  - ' 𑀳𑀫𑀢 ञच 𑀟𑁦 बच लच𑀲पच𑀟च𑀪 त𑁣ल𑀯'
- source_sentence: 𑀠च𑀟च𑀤च𑀪पच𑀯
  sentences:
  - ' धच𑀪𑀞𑁦𑀪𑀦 लचनणच𑀟 ढ𑁣𑀳प𑁣𑀟𑀯'
  - ब𑀪𑁦चपषधण𑀪च𑀠𑀢𑀣𑀯
  - 𑀠च𑀟च𑀤च𑀪पच𑀯
- source_sentence: 𑀫च𑀢𑀲𑀢 𑀳न𑀪𑁦𑀟𑀦 च
  sentences:
  - ' 𑀳𑀫त𑀫𑁦𑀪ढचप𑀢न𑀞 पच 𑀫च𑀢𑀲𑀢 ञच𑀦 𑀳न𑀪𑁦𑀟𑀦 च त𑀢𑀞𑀢𑀟 𑀭थ𑀖𑀗𑀯'
  - 𑀯
  - 𑀯
---

# SentenceTransformer based on google-bert/bert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased). 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.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 768, '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_mbert")
# Run inference
sentences = [
    '𑀫च𑀢𑀲𑀢 𑀳न𑀪𑁦𑀟𑀦 च',
    ' 𑀳𑀫त𑀫𑁦𑀪ढचप𑀢न𑀞 पच 𑀫च𑀢𑀲𑀢 ञच𑀦 𑀳न𑀪𑁦𑀟𑀦 च त𑀢𑀞𑀢𑀟 𑀭थ𑀖𑀗𑀯',
    '𑀯',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 97,043 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: 5.12 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.06 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
  | sentence_0         | sentence_1              |
  |:-------------------|:------------------------|
  | <code>च𑀞𑀱च𑀢</code> | <code> च𑀞𑀱च𑀢 𑀭ठ𑀯</code> |
  | <code>ठ𑀧𑀧𑁢𑀯</code> | <code> ठ𑀧𑀧𑁢𑀯</code>     |
  | <code>𑁢𑀗𑀯</code>   | <code>𑁢𑀗𑀯</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
- `num_train_epochs`: 5
- `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`: 5
- `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 |
|:------:|:-----:|:-------------:|
| 0.0824 | 500   | 1.1372        |
| 0.1649 | 1000  | 0.8075        |
| 0.2473 | 1500  | 0.7708        |
| 0.3297 | 2000  | 0.7464        |
| 0.4121 | 2500  | 0.7286        |
| 0.4946 | 3000  | 0.7187        |
| 0.5770 | 3500  | 0.7089        |
| 0.6594 | 4000  | 0.6942        |
| 0.7418 | 4500  | 0.7022        |
| 0.8243 | 5000  | 0.6939        |
| 0.9067 | 5500  | 0.6859        |
| 0.9891 | 6000  | 0.6807        |
| 1.0715 | 6500  | 0.6841        |
| 1.1540 | 7000  | 0.6764        |
| 1.2364 | 7500  | 0.6705        |
| 1.3188 | 8000  | 0.6712        |
| 1.4013 | 8500  | 0.6683        |
| 1.4837 | 9000  | 0.6662        |
| 1.5661 | 9500  | 0.6635        |
| 1.6485 | 10000 | 0.655         |
| 1.7310 | 10500 | 0.6667        |
| 1.8134 | 11000 | 0.6533        |
| 1.8958 | 11500 | 0.6564        |
| 1.9782 | 12000 | 0.646         |
| 2.0607 | 12500 | 0.6522        |
| 2.1431 | 13000 | 0.6466        |
| 2.2255 | 13500 | 0.6464        |
| 2.3079 | 14000 | 0.647         |
| 2.3904 | 14500 | 0.6408        |
| 2.4728 | 15000 | 0.6415        |
| 2.5552 | 15500 | 0.6397        |
| 2.6377 | 16000 | 0.6303        |
| 2.7201 | 16500 | 0.6465        |
| 2.8025 | 17000 | 0.6287        |
| 2.8849 | 17500 | 0.6358        |
| 2.9674 | 18000 | 0.6247        |
| 3.0498 | 18500 | 0.6318        |
| 3.1322 | 19000 | 0.627         |
| 3.2146 | 19500 | 0.6222        |
| 3.2971 | 20000 | 0.6262        |
| 3.3795 | 20500 | 0.6197        |
| 3.4619 | 21000 | 0.6234        |
| 3.5443 | 21500 | 0.6193        |
| 3.6268 | 22000 | 0.6088        |
| 3.7092 | 22500 | 0.624         |
| 3.7916 | 23000 | 0.6089        |
| 3.8741 | 23500 | 0.6184        |
| 3.9565 | 24000 | 0.6047        |
| 4.0389 | 24500 | 0.6066        |
| 4.1213 | 25000 | 0.6082        |
| 4.2038 | 25500 | 0.5999        |
| 4.2862 | 26000 | 0.6046        |
| 4.3686 | 26500 | 0.6038        |
| 4.4510 | 27000 | 0.5978        |
| 4.5335 | 27500 | 0.5948        |
| 4.6159 | 28000 | 0.5887        |
| 4.6983 | 28500 | 0.6031        |
| 4.7807 | 29000 | 0.5823        |
| 4.8632 | 29500 | 0.5953        |
| 4.9456 | 30000 | 0.5793        |


### 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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