README.md

---

tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:193
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: I saw someone killing a cat in the street, I felt helpless and
    sad
  sentences:
  - There is no god ?worthy of worship? except You. Glory be to You! I have certainly
    done wrong.
  - 'who say, when struck by a disaster,  Surely to Allah we belong and to Him we

    will ?all? return. '
  - And never think that Allah is unaware of what the wrongdoers do. He only delays
    them for a Day when eyes will stare [in horror]
- source_sentence: I am really sad, I hate my life and I wanna suicide
  sentences:
  - And never think that Allah is unaware of what the wrongdoers do. He only delays
    them for a Day when eyes will stare [in horror]
  - And when the ignorant address them, they say words of peace
  - And seek help through patience and prayer. Indeed, it is a burden except for the
    humble
- source_sentence: 'my cousin just died '
  sentences:
  - 'who say, when struck by a disaster,  Surely to Allah we belong and to Him we

    will ?all? return. '
  - Again, no! Never obey him ?O Prophet?! Rather, ?continue to? prostrate and draw
    near ?to Allah?.
  - Do not do a favour expecting more ?in return?.
- source_sentence: tell me about peace
  sentences:
  - O mankind, eat from whatever is on earth [that is] lawful and good and do not
    follow the footsteps of Satan. Indeed, he is to you a clear enemy
  - And when the ignorant address them, they say words of peace
  - And if you divorce them before consummating the marriage but after deciding on
    a dowry, pay half of the dowry, unless the wife graciously waives it or the husband
    graciously pays in full. Graciousness is closer to righteousness. And do not forget
    kindness among yourselves. Surely Allah is All-Seeing of what you do.
- source_sentence: I lost my friend, he died and I miss him
  sentences:
  - Not equal are the good deed and the bad deed. Repel [evil] by that [deed] which
    is better; and thereupon the one whom between you and him is enmity [will become]
    as though he was a devoted friend
  - Every soul will taste death. And you will only receive your full reward on the
    Day of Judgment. Whoever is spared from the Fire and is admitted into Paradise
    will ?indeed? triumph, whereas the life of this world is no more than the delusion
    of enjoyment.
  - Every soul will taste death, then to Us you will ?all? be returned.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---


# 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 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **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': 128, 'do_lower_case': False, 'architecture': 'BertModel'})

  (1): Pooling({'word_embedding_dimension': 384, '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})

)

```

## 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("sentence_transformers_model_id")

# Run inference

sentences = [

    'I lost my friend, he died and I miss him',

    'Every soul will taste death. And you will only receive your full reward on the Day of Judgment. Whoever is spared from the Fire and is admitted into Paradise will ?indeed? triumph, whereas the life of this world is no more than the delusion of enjoyment.',

    'Every soul will taste death, then to Us you will ?all? be returned.',

]

embeddings = model.encode(sentences)

print(embeddings.shape)

# [3, 384]



# Get the similarity scores for the embeddings

similarities = model.similarity(embeddings, embeddings)

print(similarities)

# tensor([[1.0000, 0.9072, 0.9224],

#         [0.9072, 1.0000, 0.9847],

#         [0.9224, 0.9847, 1.0000]])

```

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 193 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 193 samples:
  |         | sentence_0                                                                        | sentence_1                                                                         | label                                                         |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | float                                                         |
  | details | <ul><li>min: 5 tokens</li><li>mean: 12.27 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 39.33 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.9</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                   | sentence_1                                                                                                                                                 | label            |
  |:-------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>I am afraid that my son is not in the right way</code> | <code>And those who say: Our Lord! Grant us comfort in our spouses and our offspring, and make us leaders of the righteous</code>                          | <code>1.0</code> |
  | <code>my cat just died</code>                                | <code>And We will surely test you with something of fear and hunger and a loss of wealth and lives and fruits, but give good tidings to the patient</code> | <code>1.0</code> |
  | <code>I do not have childre</code>                           | <code>And those who say: Our Lord! Grant us comfort in our spouses and our offspring, and make us leaders of the righteous</code>                          | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json

  {

      "loss_fct": "torch.nn.modules.loss.MSELoss"

  }

  ```

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

- `num_train_epochs`: 10
- `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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 10
- `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
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch

- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: None

- `hub_always_push`: False

- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler

- `multi_dataset_batch_sampler`: round_robin

- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 5.1.1
- Transformers: 4.57.1
- PyTorch: 2.5.1
- Accelerate: 1.11.0
- Datasets: 4.3.0
- Tokenizers: 0.22.1

## 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",

}

```

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