README.md

---
base_model: Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:609
- loss:MegaBatchMarginLoss
widget:
- source_sentence: So which of the favors of your Lord would you deny
  sentences:
  - ' This is a straight path.'
  - Have they not traveled through the land and seen how was the end of those before
    them? Allah destroyed [everything] over them, and for the disbelievers is something
    comparable.
  - So which of the favors of your Lord would you deny?
- source_sentence: So would you perhaps, if you turned away, cause corruption on earth
    and sever your [ties of] relationship
  sentences:
  - Said [the king to the women], "What was your condition when you sought to seduce
    Joseph?" They said, "Perfect is Allah! We know about him no evil." The wife of
    al-'Azeez said, "Now the truth has become evident. It was I who sought to seduce
    him, and indeed, he is of the truthful.
  - Then do they not reflect upon the Qur'an, or are there locks upon [their] hearts?
  - ' Allah has not created the heavens and the earth and what is between them except
    in truth and for a specified term. And indeed, many of the people, in [the matter
    of] the meeting with their Lord, are disbelievers.'
- source_sentence: Then is he who will shield with his face the worst of the punishment
    on the Day of Resurrection [like one secure from it]
  sentences:
  - ' But you will never find in the way of Allah any change, and you will never find
    in the way of Allah any alteration.'
  - ' Then We made the sun for it an indication.'
  - ' And it will be said to the wrongdoers, "Taste what you used to earn."'
- source_sentence: Then is it the judgement of [the time of] ignorance they desire
  sentences:
  - Or do you have a clear authority?
  - And they both raced to the door, and she tore his shirt from the back, and they
    found her husband at the door. She said, "What is the recompense of one who intended
    evil for your wife but that he be imprisoned or a painful punishment?"
  - ' But who is better than Allah in judgement for a people who are certain [in faith].'
- source_sentence: Say, "Who provides for you from the heaven and the earth
  sentences:
  - Except for our first death, and we will not be punished?"
  - And gave a little and [then] refrained?
  - ' Or who controls hearing and sight and who brings the living out of the dead
    and brings the dead out of the living and who arranges [every] matter'
---

# SentenceTransformer based on Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation](https://huggingface.co/Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation). 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:** [Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation](https://huggingface.co/Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation) <!-- at revision 46d1967d948e90dde4397f342ad6ddfc99caa96a -->
- **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': False, 'pooling_mode_mean_tokens': True, '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): 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("Bofandra/fine-tuning-use-cmlm-multilingual-quran-translation-qa")
# Run inference
sentences = [
    'Say, "Who provides for you from the heaven and the earth',
    ' Or who controls hearing and sight and who brings the living out of the dead and brings the dead out of the living and who arranges [every] matter',
    'And gave a little and [then] refrained?',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 609 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: 29.19 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 29.93 tokens</li><li>max: 141 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                               | sentence_1                                                                                         |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
  | <code>And then there came to them that which they were promised</code>                                                                                                                                   | <code>Shall I inform you upon whom the devils descend?</code>                                      |
  | <code>But when the truth came to them from Us, they said, "Why was he not given like that which was given to Moses</code>                                                                                | <code>" Did they not disbelieve in that which was given to Moses before</code>                     |
  | <code>Have you not considered the assembly of the Children of Israel after [the time of] Moses when they said to a prophet of theirs, "Send to us a king, and we will fight in the way of Allah "</code> | <code> He said, "Would you perhaps refrain from fighting if fighting was prescribed for you</code> |
* Loss: [<code>MegaBatchMarginLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#megabatchmarginloss)

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

- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `num_train_epochs`: 1
- `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`: 4
- `per_device_eval_batch_size`: 4
- `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`: 1
- `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>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- 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",
}
```

#### MegaBatchMarginLoss
```bibtex
@inproceedings{wieting-gimpel-2018-paranmt,
    title = "{P}ara{NMT}-50{M}: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations",
    author = "Wieting, John and Gimpel, Kevin",
    editor = "Gurevych, Iryna and Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1042",
    doi = "10.18653/v1/P18-1042",
    pages = "451--462",
}
```

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