README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4012
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: We employed genetic, cytological, and genomic approaches to better
    understand the role of PR-Set7 and H4K20 methylation in regulating DNA replication
    and genome stability in Drosophila cells. Thus, coordinating the status of H4K20
    methylation is pivotal for the proper selection of DNA replication origins in
    higher eukaryotes. The methylation state of lysine 20 on histone H4 (H4K20) has
    been linked to chromatin compaction, transcription, DNA repair and DNA replication.
    Histone turnover is often associated with various histone modifications such as
    H3K56 acetylation (H3K56Ac), H3K36 methylation (H3K36me), and H4K20 methylation
    (H4K20me). We review the signaling pathways and functions associated with a single
    residue, H4K20, as a model chromatin and clinically important mark that regulates
    biological processes ranging from the DNA damage response and DNA replication
    to gene expression and silencing. <CopyrightInformation>© 2016 by The American
    Society for Biochemistry and Molecular Biology, Inc.</C In particular, the methylation
    states of H3K4, H3K36 and H4K20 have been associated with establishing active,
    repressed or poised origins depending on the timing and extent of methylation.
    5BrC and 5ClC may cause aberrant methylation of cytosine during DNA replication
    and mimic the endogenous methylation signal associated with gene silencing.
  sentences:
  - Is H4K20 methylation associated with DNA replication?
  - What is the function of the protein Cuf1?
  - Which syndromes are associated with heterochromia iridum?
- source_sentence: 'The Abbreviated Injury Scale (AIS) is an objective anatomically-based
    injury severity scoring system that classifies each injury by body region on a
    6 point scale. AIS is the system used to determine the Injury Severity Score (ISS)
    of the multiply injured trauma patient.


    AIS CLASSIFICATIONS

    The AIS classifies individual injuries by body region as follows:

    AIS 1 – Minor

    AIS 2 – Moderate

    AIS 3 – Serious

    AIS 4 – Severe

    AIS 5 – Critical

    AIS 6 – Maximal (currently untreatable)'
  sentences:
  - What is the role of the Hof1-Cyk3 interaction in yeast?
  - Which drugs are included in the MAID chemotherapy regimen for sarcoma?
  - What is Abbreviated Injury Scale (AIS) used to determine?
- source_sentence: Multicluster Pcdh diversity is required for mouse olfactory neural
    circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins
    are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although
    deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss
    of all three clusters (tricluster deletion) led to a severe axonal arborization
    defect and loss of self-avoidance.
  sentences:
  - Does thyroid hormone affect cardiac remodeling?
  - What are the effects of the deletion of all three Pcdh clusters (tricluster deletion)
    in mice?
  - Which R/bioconductor package has been developed to aid in epigenomic analysis?
- source_sentence: Huntington disease (HD; OMIM 143100), a progressive neurodegenerative
    disorder, is caused by an expanded trinucleotide CAG (polyQ) motif in the HTT
    gene. Mutations of the huntingtin protein (HTT) gene underlie both adult-onset
    and juvenile forms of Huntington's disease (HD).
  sentences:
  - What is resistin?
  - Does thyroid hormone signaling affect microRNAs expression in the heart?
  - What gene is mutated in Huntington's disease?
- source_sentence: Nusinersen is a modified antisense oligonucleotide that binds to
    a specific sequence in the intron, downstream of exon 7 on the pre-messenger ribonucleic
    acid (pre-mRNA) of the SMN2 gene. This modulates the splicing of the SMN2 mRNA
    transcript to include exon 7, thereby increasing the production of full-length
    SMN protein. It is approved for treatment of  spinal muscular atrophy.
  sentences:
  - Describe mechanism of action of Nusinersen.
  - What is Mobilome-seq?
  - What percentage of currently available drugs are metabolized by CYP3A4?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: sentence transformers/all mpnet base v2
      type: sentence-transformers/all-mpnet-base-v2
    metrics:
    - type: cosine_accuracy@1
      value: 0.8472418670438473
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9335219236209336
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9490806223479491
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9603960396039604
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8472418670438473
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31117397454031115
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1898161244695898
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09603960396039603
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8472418670438473
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9335219236209336
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9490806223479491
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9603960396039604
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9092929874201823
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8923284165151212
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8935812728750705
      name: Cosine Map@100
---

# 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) on the json dataset. 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:**
    - json
<!-- - **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("anoyinonion/all-mpnet-base-v2-bioasq-1epoc-batch32-100")
# Run inference
sentences = [
    'Nusinersen is a modified antisense oligonucleotide that binds to a specific sequence in the intron, downstream of exon 7 on the pre-messenger ribonucleic acid (pre-mRNA) of the SMN2 gene. This modulates the splicing of the SMN2 mRNA transcript to include exon 7, thereby increasing the production of full-length SMN protein. It is approved for treatment of  spinal muscular atrophy.',
    'Describe mechanism of action of Nusinersen.',
    'What percentage of currently available drugs are metabolized by CYP3A4?',
]
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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<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Information Retrieval

* Dataset: `sentence-transformers/all-mpnet-base-v2`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8472     |
| cosine_accuracy@3   | 0.9335     |
| cosine_accuracy@5   | 0.9491     |
| cosine_accuracy@10  | 0.9604     |
| cosine_precision@1  | 0.8472     |
| cosine_precision@3  | 0.3112     |
| cosine_precision@5  | 0.1898     |
| cosine_precision@10 | 0.096      |
| cosine_recall@1     | 0.8472     |
| cosine_recall@3     | 0.9335     |
| cosine_recall@5     | 0.9491     |
| cosine_recall@10    | 0.9604     |
| **cosine_ndcg@10**  | **0.9093** |
| cosine_mrr@10       | 0.8923     |
| cosine_map@100      | 0.8936     |

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 4,012 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                           | anchor                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 63.14 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.13 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                      | anchor                                                                                 |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
  | <code>Aberrant patterns of H3K4, H3K9, and H3K27 histone lysine methylation were shown to result in histone code alterations, which induce changes in gene expression, and affect the proliferation rate of cells in medulloblastoma.</code>                                                                                                                                  | <code>What is the implication of histone lysine methylation in medulloblastoma?</code> |
  | <code>STAG1/STAG2 proteins are tumour suppressor proteins that suppress cell proliferation and are essential for differentiation.</code>                                                                                                                                                                                                                                      | <code>What is the role of STAG1/STAG2 proteins in differentiation?</code>              |
  | <code>The association between cell phone use and incident glioblastoma remains unclear. Some studies have reported that cell phone use was associated with incident glioblastoma, and with reduced survival of patients diagnosed with glioblastoma. However, other studies have repeatedly replicated to find an association between cell phone use and glioblastoma.</code> | <code>What is the association between cell phone use and glioblastoma?</code>          |
* Loss: [<code>MultipleNegativesRankingLoss</code>](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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: 32
- `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`: 2e-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`: 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
- `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 | sentence-transformers/all-mpnet-base-v2_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:------------------------------------------------------:|
| 0.7937 | 100  | 0.1152        | 0.9093                                                 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.1.2+cu121
- Accelerate: 1.2.1
- Datasets: 2.19.1
- 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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