README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2437
- loss:ContrastiveLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: I am having troubles and confusing moments with my body and I am
    scared I may be pregnant by my research online and I really want some advice ?
  sentences:
  - 'Does Acyclovir cause ulcers when it is prescribed for genital herpes? '
  - The confusing symptoms and online research points towards me being pregnant. Can
    I get a professional advice?
  - Do bariatric surgeries like gastric sleeve or Roux-en-Y surgery actually work
    in the long term?
- source_sentence: It started with a headache the next day came dizziness when I move
    my eyes, soreness behind my eyes, 102 fever, slight cough. Help!
  sentences:
  - I had a headache and this was followe by dizziness on moving the eyes, soreness
    behind my eyes, high grade fever (102) and slight cough. Can you help me?
  - What are the signs of ovulation?
  - Why does it hurt when I shave my face? Can I do something else for it besides
    shaving in the direction of the hair growth?
- source_sentence: How low can hemoglobin go before you need a transfusion?
  sentences:
  - 'I heard banana is rich in potassium. I am having diarrhea and can I take banana. '
  - At what Hemoglobin levels, is a blood transfusion recommended?
  - What are the symptoms of eye cancer?
- source_sentence: I'm 5 weeks pregnant and this morning had brownish spotting, my
    gyn said this is normal and ita was due to implantation, should I be worried?
  sentences:
  - I have abdominal cramps, spotting, nause and fatigue. I am on oral contraceptive
    pills. I take them regularly. My pregnancy test is negative. I dont believe it
    is implantation as I am not pregnant. Could it be withdrawal bleeding or do I
    have an STD?
  - 'What''s best for a 1 year old, breast milk or bottle milk? '
  - I am 40, and I've had a breast lump in my right breast for about 4 years now.
    Could it be cancer?
- source_sentence: My bm aren't solid but not quite loose. Looks more like for lack
    of better word "shredded" the why is this?
  sentences:
  - I have been taking treatment for anxiety and depression. I was given a new medication
    and have experienced heart flutters, can this medication cause it?
  - You might think I'm a bit paranoid but could you please help me with the five
    most common emergency surgeries in american teen girls?
  - What causes stringy and shredded stools?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: all mqp test
      type: all-mqp-test
    metrics:
    - type: cosine_accuracy
      value: 0.8786885245901639
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7678120136260986
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8796147672552167
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7446306943893433
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.8810289389067524
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8782051282051282
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9474266832530879
      name: Cosine Ap
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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("mpnet-base-all-mqp-binary")
# Run inference
sentences = [
    'My bm aren\'t solid but not quite loose. Looks more like for lack of better word "shredded" the why is this?',
    'What causes stringy and shredded stools?',
    'I have been taking treatment for anxiety and depression. I was given a new medication and have experienced heart flutters, can this medication cause it?',
]
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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## Evaluation

### Metrics

#### Binary Classification

* Dataset: `all-mqp-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | Value      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.8787     |
| cosine_accuracy_threshold | 0.7678     |
| cosine_f1                 | 0.8796     |
| cosine_f1_threshold       | 0.7446     |
| cosine_precision          | 0.881      |
| cosine_recall             | 0.8782     |
| **cosine_ap**             | **0.9474** |

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### Recommendations

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 2,437 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | text1                                                                             | text2                                                                              | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                             | int                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 26.53 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 28.18 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>0: ~49.00%</li><li>1: ~51.00%</li></ul> |
* Samples:
  | text1                                                                                                                                | text2                                                                                                                         | label          |
  |:-------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>I discovered I get this weakness in my hand whenever I try to snap my fingers, slight pain runs across elbow and wrist?</code> | <code>When I try to snap my fingers there is weakness and pain across elbow and wrist? May I know what are the causes?</code> | <code>1</code> |
  | <code>If a mother has celiac should the daughter be tested?</code>                                                                   | <code>What is Celiac disease?</code>                                                                                          | <code>0</code> |
  | <code>Hi im 18 and I would like to know what I would use or take to get taller?</code>                                               | <code>Can growth hormone taken in minimal quantities increase height after 21 years in a male?</code>                         | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 610 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 610 samples:
  |         | text1                                                                             | text2                                                                             | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 27.56 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.88 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>0: ~48.85%</li><li>1: ~51.15%</li></ul> |
* Samples:
  | text1                                                                                                                                                      | text2                                                                                                                                                                                                                                   | label          |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Okay so i'm on bc and I have had sex (it hurts) i'm bleeding brown and my vagina hurts almost itchy but it hurts?</code>                             | <code>I noticed a brown discharge and itching in my vaginal area to the point that it hurts. I am also on birth control and have sexual intercourse. What do you think is causing this?</code>                                          | <code>1</code> |
  | <code>I've had body aches, blocked stuffy nose, headaches, pressure in my face and throat tightness and it feels dry for 6 months is it a bad cold?</code> | <code>For the last 6 months, I've noticed symptoms like body aches, stuffy nose, headaches, pressure sensation in the face, throat tightness and feels dry. Can a cold last this long or should I be looking for something else?</code> | <code>1</code> |
  | <code>Is there any way to stop my period for a little while without a prescription?</code>                                                                 | <code>Are there any natural ways to stop my period without having to visit a local doctor?</code>                                                                                                                                       | <code>1</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `push_to_hub`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `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`: True
- `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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | all-mqp-test_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:----------------------:|
| 0.6536 | 100  | 0.0137        | 0.0135          | -                      |
| 1.0    | 153  | -             | -               | 0.9474                 |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.6.0+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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