README.md

---
base_model: sentence-transformers/LaBSE
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:23999
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Who led thee through that great and terrible wilderness , wherein
    were fiery serpents , and scorpions , and drought , where there was no water  ;
    who brought thee forth water out of the rock of flint  ;
  sentences:
  - bad u ai ïa ki ha u Aaron bad ki khun shynrang jong u .
  - U la ïalam ïa phi lyngba ka ri shyiap kaba ïar bad kaba ishyrkhei eh , ha kaba
    la don ki bseiñ kiba don bih bad ki  ñianglartham . Ha kata ka ri kaba tyrkhong
    bad ka bym don um , u la pynmih um na u mawsiang na ka bynta jong phi .
  - Ki paidbah na ki jait ba na shatei ki phah khot ïa u , bad nangta ma ki baroh
    ki ïaleit lang sha u Rehoboam bad ki ong ha u ,
- source_sentence: And , behold , Boaz came from Beth–lehem , and said unto the reapers
    , The  Lord  be with you . And they answered him , The  Lord  bless thee .
  sentences:
  - Ko ki briew bymïaineh , to wan noh ; phi long ki jong nga . Ngan shim iwei na
    phi na kawei kawei ka shnong bad ar ngut na kawei kawei ka kur , bad ngan wallam
    pat ïa phi sha u lum Seïon .
  - Hadien katto katne por u Boas da lade hi u wan poi na Bethlehem bad u ai khublei
    ïa ki nongtrei .  To U  Trai  un long ryngkat bad phi !  u ong .  U  Trai  u kyrkhu
    ïa phi !  ki jubab .
  - U Trai u la ong ha u ,  Khreh bad leit sha ‘ Ka Lynti Ba-beit ,’ bad ha ka ïing
    jong u Judas kylli ïa u briew na Tarsos uba kyrteng u Saul .
- source_sentence: Jehovah used the prehuman Jesus as his “master worker” in creating
    all other things in heaven and on earth .
  sentences:
  - Shuwa ba un wan long briew U Jehobah u la pyndonkam ïa u Jisu kum u “rangbah nongtrei”
    ha kaba thaw ïa kiei kiei baroh kiba don ha bneng bad ha khyndew .
  - Shisien la don u briew uba la leit ban bet symbai . Katba u dang bet ïa u symbai
    , katto katne na u , ki la hap ha shi lynter ka lynti ïaid kjat , ha kaba ki la
    shah ïuh , bad ki sim ki la bam lut .
  - Ngan ïathuh ïa ka shatei ban shah ïa ki ban leit bad ïa ka shathie ban ym bat
    noh ïa ki . Ai ba ki briew jong nga ki wan phai na ki ri bajngai , na man la ki
    bynta baroh jong ka pyrthei .
- source_sentence: 'The like figure whereunto even baptism doth also now save us (
    not the putting away of the filth of the flesh , but the answer of a good conscience
    toward God , ) by the resurrection of Jesus Christ :'
  sentences:
  - kaba long ka dak kaba kdew sha ka jingpynbaptis , kaba pyllait im ïa phi mynta
    . Kam dei ka jingsait noh ïa ka jakhlia na ka met , hynrei ka jingkular ba la
    pynlong sha U Blei na ka jingïatiplem babha . Ka pynim ïa phi da ka jingmihpat
    jong U Jisu Khrist ,
  - Ki briew kiba sniew kin ïoh ïa kaei kaba ki dei ban ïoh . Ki briew kiba bha kin
    ïoh bainong na ka bynta ki kam jong ki .
  - Nangta nga la ïohi ïa ka bneng bathymmai bad ïa ka pyrthei bathymmai . Ka bneng
    banyngkong bad ka pyrthei banyngkong ki la jah noh , bad ka duriaw kam don shuh
    .
- source_sentence: On that day they read in the book of Moses in the audience of the
    people  ; and therein was found written , that the Ammonite and the Moabite should
    not come into the congregation of God for ever  ;
  sentences:
  - U Elisha u la ïap bad la tep ïa u . Man la ka snem ki kynhun jong ki Moab ki ju
    wan tur thma ïa ka ri Israel .
  - Katba dang pule jam ïa ka Hukum u Moses ha u paidbah , ki poi ha ka bynta kaba
    ong ba ym dei ban shah ïa u nong Amon ne u nong Moab ban ïasnohlang bad ki briew
    jong U Blei .
  - U angel u la jubab ,  U Mynsiem Bakhuid un sa wan ha pha , bad ka bor jong U Blei
    kan shong halor jong pha . Na kane ka daw , ïa i khunlung bakhuid yn khot U Khun
    U Blei .
---

# SentenceTransformer based on sentence-transformers/LaBSE

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision e34fab64a3011d2176c99545a93d5cbddc9a91b7 -->
- **Maximum Sequence Length:** 256 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': 256, '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})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): Normalize()
)
```

## 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("ABHIiiii1/LaBSE-Fine-Tuned-EN-KHA")
# Run inference
sentences = [
    'On that day they read in the book of Moses in the audience of the people  ; and therein was found written , that the Ammonite and the Moabite should not come into the congregation of God for ever  ;',
    'Katba dang pule jam ïa ka Hukum u Moses ha u paidbah , ki poi ha ka bynta kaba ong ba ym dei ban shah ïa u nong Amon ne u nong Moab ban ïasnohlang bad ki briew jong U Blei .',
    'U Elisha u la ïap bad la tep ïa u . Man la ka snem ki kynhun jong ki Moab ki ju wan tur thma ïa ka ri Israel .',
]
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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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: 23,999 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: 6 tokens</li><li>mean: 34.89 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 51.51 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                          |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>And Moses went out from Pharaoh , and entreated the  Lord .</code>                                                                                                                                          | <code>U Moses u mihnoh na u Pharaoh , bad u kyrpad ïa U  Trai  ,</code>                                                                                                                                                                                                                                                             |
  | <code>In the ninth year of Hoshea the king of Assyria took Samaria , and carried Israel away into Assyria , and placed them in Halah and in Habor by the river of Gozan , and in the cities of the Medes .</code> | <code>kaba long ka snem kaba khyndai jong ka jingsynshar u Hoshea , u patsha ka Assyria u kurup ïa ka Samaria , u rah ïa ki Israel sha Assyria kum ki koidi , bad pynsah katto katne ngut na ki ha ka nongbah Halah , katto katne pat hajan ka wah Habor ha ka distrik Gosan , bad katto katne ha ki nongbah jong ka Media .</code> |
  | <code>And the king said unto Cushi , Is the young man Absalom safe ? And Cushi answered , The enemies of my lord the king , and all that rise against thee to do thee hurt , be as that young man is .</code>     | <code>Hato u samla Absalom u dang im ?  u syiem u kylli . U mraw u jubab ,  Ko Kynrad , nga sngew ba kaei kaba la jia ha u kan jin da la jia ha baroh ki nongshun jong ngi , bad ha baroh kiba ïaleh pyrshah ïa phi .</code>                                                                                                        |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### 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 |
|:------:|:----:|:-------------:|
| 0.3333 | 500  | 0.542         |
| 0.6667 | 1000 | 0.135         |
| 1.0    | 1500 | 0.0926        |
| 1.3333 | 2000 | 0.0535        |
| 1.6667 | 2500 | 0.0226        |
| 2.0    | 3000 | 0.018         |
| 2.3333 | 3500 | 0.0124        |
| 2.6667 | 4000 | 0.0057        |
| 3.0    | 4500 | 0.0053        |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.2
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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",
}
```

#### 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},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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