---
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)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 23,999 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 6 tokens
- mean: 34.89 tokens
- max: 87 tokens
| - min: 8 tokens
- mean: 51.51 tokens
- max: 127 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| And Moses went out from Pharaoh , and entreated the Lord . | U Moses u mihnoh na u Pharaoh , bad u kyrpad ïa U Trai , |
| 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 . | 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 . |
| 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 . | 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 . |
* Loss: [MultipleNegativesRankingLoss](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
Click to expand
- `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