retrieval-mpnet-dot-finetuned-combined-synthetic-dataset

SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1

This is a sentence-transformers model finetuned from sentence-transformers/multi-qa-mpnet-base-dot-v1. 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/multi-qa-mpnet-base-dot-v1 <!-- at revision 3af7c6da5b3e1bea796ef6c97fe237538cbe6e7f -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Dot Product

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'nerve cell dysfunction, riboflavin deficiency',
    'Riboflavin transporter deficiency neuronopathy is a disorder that affects nerve cells (neurons). Affected individuals typically have hearing loss caused by nerve damage in the inner ear (sensorineural hearing loss) and signs of damage to other nerves.',
    'A number sign (#) is used with this entry because hyperprolinemia type I (HYRPRO1) is caused by homozygous or compound heterozygous mutation in the proline dehydrogenase gene (PRODH; 606810) on chromosome 22q11.\n\nThe PRODH gene falls within the region deleted in the 22q11 deletion syndrome, including DiGeorge syndrome (188400) and velocardiofacial syndrome (192430).\n\nDescription\n\nPhang et al. (2001) noted that prospective studies of HPI probands identified through newborn screening as well as reports of several families have suggested that it is a metabolic disorder not clearly associated with clinical manifestations. Phang et al. (2001) concluded that HPI is a relatively benign condition in most individuals under most circumstances. However, other reports have suggested that some patients have a severe phenotype with neurologic manifestations, including epilepsy and mental retardation (Jacquet et al., 2003).\n\n### Genetic Heterogeneity of Hyperprolinemia',
]
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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Information Retrieval

• Evaluated with <code>InformationRetrievalEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 95,159 training samples
• Columns: <code>queries</code> and <code>chunks</code>
• Approximate statistics based on the first 1000 samples:

  	queries	chunks
  type	string	string
  details	<ul><li>min: 5 tokens</li><li>mean: 15.01 tokens</li><li>max: 30 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 158.91 tokens</li><li>max: 319 tokens</li></ul>

• Samples:

  queries	chunks
  <code>hypotrichosis, wiry hair, onycholysis</code>	<code>Green et al. (2003) reported an Australian family in which 22 members over 4 generations had progressive patterned scalp hypotrichosis and wiry hair similar to that seen in Marie Unna hereditary hypotrichosis (MUHH; 146550). Features differing from those of MUHH included absence of signs of abnormality at birth, relative sparing of body hair, distal onycholysis, and intermittent cosegregation with autosomal dominant cleft lip and palate. Five individuals had associated cleft lip and palate. Green et al. (2003) excluded linkage of the disorder in the Australian family to the MUHH locus on chromosome 8p21.</code>
  <code>cleft lip, cleft palate, hair loss</code>	<code>Green et al. (2003) reported an Australian family in which 22 members over 4 generations had progressive patterned scalp hypotrichosis and wiry hair similar to that seen in Marie Unna hereditary hypotrichosis (MUHH; 146550). Features differing from those of MUHH included absence of signs of abnormality at birth, relative sparing of body hair, distal onycholysis, and intermittent cosegregation with autosomal dominant cleft lip and palate. Five individuals had associated cleft lip and palate. Green et al. (2003) excluded linkage of the disorder in the Australian family to the MUHH locus on chromosome 8p21.</code>
  <code>progressive patterned scalp, autosomal dominant inheritance</code>	<code>Green et al. (2003) reported an Australian family in which 22 members over 4 generations had progressive patterned scalp hypotrichosis and wiry hair similar to that seen in Marie Unna hereditary hypotrichosis (MUHH; 146550). Features differing from those of MUHH included absence of signs of abnormality at birth, relative sparing of body hair, distal onycholysis, and intermittent cosegregation with autosomal dominant cleft lip and palate. Five individuals had associated cleft lip and palate. Green et al. (2003) excluded linkage of the disorder in the Australian family to the MUHH locus on chromosome 8p21.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 1,
      "similarity_fct": "dot_score"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 8,747 evaluation samples
• Columns: <code>queries</code> and <code>chunks</code>
• Approximate statistics based on the first 1000 samples:

  	queries	chunks
  type	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 14.71 tokens</li><li>max: 31 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 155.81 tokens</li><li>max: 305 tokens</li></ul>

• Samples:

  queries	chunks
  <code>white patches, corrugated tongue, immunocompromised, Epstein-Barr virus</code>	<code>Not to be confused with Hairy tongue.<br><br>Hairy leukoplakia <br>Other namesOral hairy leukoplakia,[1]:385 OHL, or HIV-associated hairy leukoplakia[2] <br>SpecialtyGastroenterology <br> <br>Hairy leukoplakia is a white patch on the side of the tongue with a corrugated or hairy appearance. It is caused by Epstein-Barr virus (EBV) and occurs usually in persons who are immunocompromised, especially those with human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS). The white lesion, which cannot be scraped off, is benign and does not require any treatment, although its appearance may have diagnostic and prognostic implications for the underlying condition.<br><br>Depending upon what definition of leukoplakia is used, hairy leukoplakia is sometimes considered a subtype of leukoplakia, or a distinct diagnosis.<br><br>## Contents</code>
  <code>HIV-associated lesions, oral hairy leukoplakia, benign white lesions, tongue appearance</code>	<code>Not to be confused with Hairy tongue.<br><br>Hairy leukoplakia <br>Other namesOral hairy leukoplakia,[1]:385 OHL, or HIV-associated hairy leukoplakia[2] <br>SpecialtyGastroenterology <br> <br>Hairy leukoplakia is a white patch on the side of the tongue with a corrugated or hairy appearance. It is caused by Epstein-Barr virus (EBV) and occurs usually in persons who are immunocompromised, especially those with human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS). The white lesion, which cannot be scraped off, is benign and does not require any treatment, although its appearance may have diagnostic and prognostic implications for the underlying condition.<br><br>Depending upon what definition of leukoplakia is used, hairy leukoplakia is sometimes considered a subtype of leukoplakia, or a distinct diagnosis.<br><br>## Contents</code>
  <code>hairy leukoplakia symptoms, non-scrapable lesions, HIV/AIDS, oral lesions</code>	<code>Not to be confused with Hairy tongue.<br><br>Hairy leukoplakia <br>Other namesOral hairy leukoplakia,[1]:385 OHL, or HIV-associated hairy leukoplakia[2] <br>SpecialtyGastroenterology <br> <br>Hairy leukoplakia is a white patch on the side of the tongue with a corrugated or hairy appearance. It is caused by Epstein-Barr virus (EBV) and occurs usually in persons who are immunocompromised, especially those with human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS). The white lesion, which cannot be scraped off, is benign and does not require any treatment, although its appearance may have diagnostic and prognostic implications for the underlying condition.<br><br>Depending upon what definition of leukoplakia is used, hairy leukoplakia is sometimes considered a subtype of leukoplakia, or a distinct diagnosis.<br><br>## Contents</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 1,
      "similarity_fct": "dot_score"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• learning_rate: 2e-05
• num_train_epochs: 15
• warmup_ratio: 0.1
• fp16: True
• load_best_model_at_end: True
• eval_on_start: 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: 32
• 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: 15
• 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: True
• 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: True
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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: True
• eval_use_gather_object: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

<details><summary>Click to expand</summary>

• The bold row denotes the saved checkpoint.

</details>

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 3.0.1
• Transformers: 4.43.3
• PyTorch: 2.3.1+cu121
• Accelerate: 0.30.1
• Datasets: 2.19.2
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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

@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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