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shashrao54/embedding-finetuning-model

sourceHugging Faceupdated 4mo agoView on Hugging Face
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Model Card

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 <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
  • —Maximum Sequence Length: 384 tokens
  • —Output Dimensionality: 768 dimensions
  • —Similarity Function: Cosine Similarity
  • —Supported Modality: Text <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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:

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("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]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset
  • —Size: 8 training samples
  • —Columns: <code>anchor</code> and <code>positive</code>
  • —Approximate statistics based on the first 8 samples: | | anchor | positive | |:---------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | <ul><li>min: 8 tokens</li><li>mean: 9.88 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 57 tokens</li><li>mean: 220.5 tokens</li><li>max: 384 tokens</li></ul> |
  • —Samples: | anchor | positive | |:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Explain amoxycillin in this document.</code> | <code>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...</code> | | <code>Explain renal in this document.</code> | <code>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...</code> | | <code>What are the key points about renal?</code> | <code>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...</code> |
  • —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:
json
  {
      "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

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

  • —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: adamwtorchfused
  • —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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: {}

</details>

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
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{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},
}

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