README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:40906
- loss:MatryoshkaLoss
- loss:MegaBatchMarginLoss
widget:
- source_sentence: >-
    One of three laminate structures that form the spindle pole body; the inner
    plaque is in the nucleus.
  sentences:
  - >-
    maturation of SSU-rRNA from tetracistronic rRNA transcript (SSU-rRNA, 5.8S
    rRNA, 2S rRNA, LSU-rRNA)
  - leukotriene receptor activity
  - inner plaque of spindle pole body
- source_sentence: >-
    The covalent attachment of a myristoyl group to the N-terminal amino acid
    residue of a protein.
  sentences:
  - MHC class I protein complex assembly
  - N-terminal protein myristoylation
  - neurotrophin receptor activity
- source_sentence: >-
    The inner, i.e. lumen-facing, lipid bilayer of the plastid envelope; also
    faces the plastid stroma.
  sentences:
  - plastid inner membrane
  - neuron migration involved in retrograde extension
  - stomatal complex morphogenesis
- source_sentence: >-
    Initiation of a region of tissue in a plant that is composed of one or more
    undifferentiated cells capable of undergoing mitosis and differentiation,
    thereby effecting growth and development of a plant by giving rise to more
    meristem or specialized tissue.
  sentences:
  - meristem initiation
  - polytene chromosome
  - cardiac ventricle development
- source_sentence: >-
    The sex chromosome present in both sexes of species in which the male is the
    heterogametic sex. Two copies of the X chromosome are present in each
    somatic cell of females and one copy is present in males.
  sentences:
  - establishment of cell polarity involved in gastrulation cell migration
  - X chromosome
  - somatic diversification of immune receptors by N region addition
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: translation
      name: Translation
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: src2trg_accuracy
      value: 0.00015186028853454822
      name: Src2Trg Accuracy
    - type: trg2src_accuracy
      value: 0
      name: Trg2Src Accuracy
    - type: mean_accuracy
      value: 0.00007593014426727411
      name: Mean Accuracy
license: apache-2.0
datasets:
- NothingMuch/GO-Terms
language:
- en
base_model:
- Snowflake/snowflake-arctic-embed-m-v1.5
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained on the parquet dataset. It maps sentences & paragraphs to a 128-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 128 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - parquet
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("GO-Term-Embeddings")
# Run inference
sentences = [
    'The sex chromosome present in both sexes of species in which the male is the heterogametic sex. Two copies of the X chromosome are present in each somatic cell of females and one copy is present in males.',
    'X chromosome',
    'somatic diversification of immune receptors by N region addition',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 128]

# 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

#### Translation

* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)

| Metric            | Value      |
|:------------------|:-----------|
| src2trg_accuracy  | 0.0002     |
| trg2src_accuracy  | 0.0        |
| **mean_accuracy** | **0.0001** |

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## Training Details

### Training Dataset

#### parquet

* Dataset: parquet
* Size: 40,906 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 43.8 tokens</li><li>max: 193 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.19 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                          | positive                                                                 |
  |:------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
  | <code>Catalysis of the transfer of a mannose residue to an oligosaccharide, forming an alpha-(1->6) linkage.</code>                             | <code>1,6-alpha-mannosyltransferase activity</code>                      |
  | <code>Catalysis of the hydrolysis of ester linkages within a single-stranded deoxyribonucleic acid molecule by creating internal breaks.</code> | <code>single-stranded DNA specific endodeoxyribonuclease activity</code> |
  | <code>Catalysis of the hydrolysis of ester linkages within a single-stranded deoxyribonucleic acid molecule by creating internal breaks.</code> | <code>ssDNA-specific endodeoxyribonuclease activity</code>               |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MegaBatchMarginLoss",
      "matryoshka_dims": [
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### parquet

* Dataset: parquet
* Size: 6,585 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 42.75 tokens</li><li>max: 296 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.62 tokens</li><li>max: 36 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                                                                               | positive                                      |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------|
  | <code>The maintenance of the structure and integrity of the mitochondrial genome; includes replication and segregation of the mitochondrial chromosome.</code>                                                                                                       | <code>mitochondrial genome maintenance</code> |
  | <code>The repair of single strand breaks in DNA. Repair of such breaks is mediated by the same enzyme systems as are used in base excision repair.</code>                                                                                                            | <code>single strand break repair</code>       |
  | <code>Any process that modulates the frequency, rate or extent of DNA recombination, a DNA metabolic process in which a new genotype is formed by reassortment of genes resulting in gene combinations different from those that were present in the parents.</code> | <code>regulation of DNA recombination</code>  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MegaBatchMarginLoss",
      "matryoshka_dims": [
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 5
- `torch_empty_cache_steps`: 200
- `learning_rate`: 0.2
- `weight_decay`: 0.001
- `num_train_epochs`: 1
- `warmup_ratio`: 0.25
- `seed`: 25
- `batch_sampler`: no_duplicates

#### 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`: 10
- `per_device_eval_batch_size`: 5
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: 200
- `learning_rate`: 0.2
- `weight_decay`: 0.001
- `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`: 0.25
- `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`: 25
- `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`: 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 | mean_accuracy |
|:-----:|:----:|:-------------:|
| 0     | 0    | 0.0001        |


### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1
- Accelerate: 1.2.0
- Datasets: 3.1.0
- 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### 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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