shashrao54/embedding-finetuning-model

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

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-mpnet-base-v2
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'MPNetModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({})
)

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("shashrao54/embedding-finetuning-model")
# Run inference
sentences = [
    'Explain conforms in this document.',
    'Identification: conforms to reference standard. - Related substances: within limits. - Microbial limits: complies with pharmacopeial requirements. Packaging: Alu-Alu blister packs; carton labeling includes batch number and expiry date.',
    'Identification: conforms to reference standard. - Related substances: within limits. - Microbial limits: complies with pharmacopeial requirements. Packaging: Alu-Alu blister packs; carton labeling includes batch number and expiry date.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.2568, 0.2568],
#         [0.2568, 1.0000, 1.0000],
#         [0.2568, 1.0000, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 8 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 8 samples:

  	anchor	positive
  type	string	string
  modality	text	text
  details	min: 8 tokens mean: 9.88 tokens max: 12 tokens	min: 57 tokens mean: 220.5 tokens max: 384 tokens

• Samples:

  anchor	positive
  Explain amoxycillin in this document.	PHARMA PRODUCT DOSSIER (SYNTHETIC) Product: Amoxycillin Capsules 500 mg Indication: Treatment of bacterial infections (upper respiratory tract, urinary tract). Mechanism of Action: Beta-lactam antibiotic; inhibits bacterial cell wall synthesis. Contraindications: Hypersensitivity to penicillins; history of severe allergy. Warnings: Risk of anaphylaxis; monitor for rash; adjust dose in renal impairment. Dosage & Administration: - Adults: 500 mg every 8 hours. - Renal impairment: dose adjustment required based on creatinine clearance. Adverse Reactions: - Common: nausea, diarrhea, rash. - Serious: anaphylaxis, C. difficile-associated diarrhea (rare). CLINICAL OVERVIEW (SYNTHETIC) Clinical Summary: Efficacy shown in randomized controlled trials for susceptible organisms. Non-inferiority demonstrated versus comparator antibiotics in mild-to-moderate infections. Drug Interactions: - Probenecid increases amoxycillin levels by decreasing renal clearance. - Oral anticoagulants: monitor INR; ma...
  Explain renal in this document.	PHARMA PRODUCT DOSSIER (SYNTHETIC) Product: Amoxycillin Capsules 500 mg Indication: Treatment of bacterial infections (upper respiratory tract, urinary tract). Mechanism of Action: Beta-lactam antibiotic; inhibits bacterial cell wall synthesis. Contraindications: Hypersensitivity to penicillins; history of severe allergy. Warnings: Risk of anaphylaxis; monitor for rash; adjust dose in renal impairment. Dosage & Administration: - Adults: 500 mg every 8 hours. - Renal impairment: dose adjustment required based on creatinine clearance. Adverse Reactions: - Common: nausea, diarrhea, rash. - Serious: anaphylaxis, C. difficile-associated diarrhea (rare). CLINICAL OVERVIEW (SYNTHETIC) Clinical Summary: Efficacy shown in randomized controlled trials for susceptible organisms. Non-inferiority demonstrated versus comparator antibiotics in mild-to-moderate infections. Drug Interactions: - Probenecid increases amoxycillin levels by decreasing renal clearance. - Oral anticoagulants: monitor INR; ma...
  What are the key points about renal?	PHARMA PRODUCT DOSSIER (SYNTHETIC) Product: Amoxycillin Capsules 500 mg Indication: Treatment of bacterial infections (upper respiratory tract, urinary tract). Mechanism of Action: Beta-lactam antibiotic; inhibits bacterial cell wall synthesis. Contraindications: Hypersensitivity to penicillins; history of severe allergy. Warnings: Risk of anaphylaxis; monitor for rash; adjust dose in renal impairment. Dosage & Administration: - Adults: 500 mg every 8 hours. - Renal impairment: dose adjustment required based on creatinine clearance. Adverse Reactions: - Common: nausea, diarrhea, rash. - Serious: anaphylaxis, C. difficile-associated diarrhea (rare). CLINICAL OVERVIEW (SYNTHETIC) Clinical Summary: Efficacy shown in randomized controlled trials for susceptible organisms. Non-inferiority demonstrated versus comparator antibiotics in mild-to-moderate infections. Drug Interactions: - Probenecid increases amoxycillin levels by decreasing renal clearance. - Oral anticoagulants: monitor INR; ma...

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• learning_rate: 2e-05
• warmup_steps: 0.1
• fp16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• per_device_train_batch_size: 8
• num_train_epochs: 3
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 8
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Time

• Training: 2.5 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.5.1
• Transformers: 5.10.1
• PyTorch: 2.11.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}