README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:20000
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: 'Question: Is this describing a (1) directly correlative relationship,
    (2) conditionally causative relationship, (3) causative relationship, or (0) no
    relationship.'
  sentences:
  - 'C: Iron deficiency anemia in the mother; normal Hb levels in the fetus'
  - This is a conditionally causative relationship
  - 'C: Decreasing carbohydrate intake, increasing fat intake'
- source_sentence: Please summerize the given abstract to a title
  sentences:
  - 'BatteryLab: A Collaborative Platform for Power Monitoring'
  - hi ! good evening. i am chatbot answering your query. from the history, it seems
    that you might have sustained some kind of trivial trauma while cutting woods
    resulting in oozing of blood in the tissue forming a collection of blood (hematoma).
    usually, small collections of blood get absorbed of their own. however, this may
    not happen in cases where the blood clotting is hampered by the intake of blood
    thinners as is in your case and the same might also get infected causing more
    pain due to an abscess. if i were your doctor, i would consult your physician
    who started your blood thinning agent for consideration of discontinuing these
    medicines for some time till it heals up. if it does not even then, i would refer
    you to a general surgeon for a clinical examination and further management. i
    hope this information would help you in discussing with your family physician/treating
    doctor in further management of your problem. please do not hesitate to ask in
    case of any further doubts. thanks for choosing chatbot to clear doubts on your
    health problems. wishing you an early recovery. chatbot. if i were your doctor,
  - Effects of the psychoactive compounds in green tea on risky decision-making.
- source_sentence: Answer this question truthfully
  sentences:
  - Laparoscopic stomach-partitioning gastrojejunostomy with reduced-port techniques
    for unresectable distal gastric cancer.
  - hi, thanks for posting the query, i would suggest you to get an x-ray of the tooth
    piece left in the socket, according to your clinical symptoms i suppose that you
    might have developed an infection in the region which is radiating in the nearby
    tooth region giving you such feeling, also take course of antibiotics and analgesics,
    maintain a good oral hygiene, take lukewarm saline and antiseptic mouthwash rinses,
    take an appointment with oral surgeon and get the piece removed. hope you find
    this as helpful, take care!
  - If you feel you are developing symptoms suggestive of Pneumocystis pneumonia contact
    your health professional.
- source_sentence: If you are a doctor, please answer the medical questions based
    on the patient's description.
  sentences:
  - Hazard control for communicable disease transport at Ornge
  - hello and thank you for asking chatbot, i understand your concern. you are probably
    experiencing low blood pressure when you stand up, called orthostatic hypotension.
    as a result, not enough blood reaches your brain, and you feel lightheaded or
    dizzy. here are some advices
  - hi, thank you for posting your query. i have noted your symptoms. these are suggestive
    of sciatica, or nerve compression in the lower back region due to slipped disc
    in that location. disc prolapse leads to compression of the nerves, resulting
    in low back pain, leg pain and tingling. symptoms may increase on walking. the
    diagnosis can be confirmed by doing mri scan of the lumbosacral spine. good medical
    treatments are available for this condition. i hope my answer helps. please get
    back if you have any follow-up queries or if you require any additional information.
    wishing you good health, chatbot. ly/
- source_sentence: Please summerize the given abstract to a title
  sentences:
  - Gastric mucormycosis with splenic invasion a rare abdominal complication of COVID-19
    pneumonia
  - 'Russian-Language Mobile Apps for Reducing Alcohol Use: Systematic Search and
    Evaluation'
  - Peacekeeping after Covid-19
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}) with Transformer model: 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 = [
    'Please summerize the given abstract to a title',
    'Peacekeeping after Covid-19',
    'Russian-Language Mobile Apps for Reducing Alcohol Use: Systematic Search and Evaluation',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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: 20,000 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                         | label                                                         |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | float                                                         |
  | details | <ul><li>min: 7 tokens</li><li>mean: 15.87 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 77.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                  | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | label            |
  |:------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>Please summerize the given abstract to a title</code> | <code>Impact of National Containment Measures on Decelerating the Increase in Daily New Cases of COVID-19 in 54 Countries and 4 Epicenters of the Pandemic: Comparative Observational Study</code>                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>1.0</code> |
  | <code>Answer this question truthfully</code>                | <code>Intracranial hypertension is defined as ICP greater than 20 mmHg. This condition occurs when there is increased pressure inside the skull, which can cause a range of symptoms and potentially lead to serious complications such as brain damage or herniation. Intracranial hypertension can be caused by a variety of factors, including head injury, brain tumors, infections, and certain medications. Treatment options may include medications to reduce pressure, surgery to relieve pressure or address underlying causes, or other supportive measures to manage symptoms and prevent complications.</code> | <code>1.0</code> |
  | <code>Answer this question truthfully</code>                | <code>The bone marrow is a rapidly proliferating population of cells that produces blood cells, including white blood cells, red blood cells, and platelets. 6-mercaptopurine and azathioprine are medications that are commonly used to treat autoimmune diseases and some types of cancer. However, because these drugs interfere with the production of new cells, they can also cause myelosuppression, which is a condition in which the bone marrow produces fewer blood cells than normal. This can lead to a variety of symptoms, including fatigue, weakness, and an increased risk of infection.</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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 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
- `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`: 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}
- `tp_size`: 0
- `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`: None
- `hub_always_push`: False
- `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`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | Training Loss |
|:-----:|:----:|:-------------:|
| 0.4   | 500  | 0.4093        |
| 0.8   | 1000 | 0.0074        |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1

## 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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