SentenceTransformer based on FremyCompany/BioLORD-2023-M

This is a sentence-transformers model finetuned from FremyCompany/BioLORD-2023-M. 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: FremyCompany/BioLORD-2023-M
• Maximum Sequence Length: 64 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': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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 = [
    'Leukemia',
    'test mein blood cancer bataya hai, safed khoon (WBC) count abnormal hai',
    'मला मूळव्याध झाला आहे आणि बसायला त्रास होतोय',
]
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, 0.7930, 0.4414],
#         [0.7930, 1.0000, 0.1904],
#         [0.4414, 0.1904, 1.0000]], dtype=torch.bfloat16)

Evaluation

📊 RAG Retrieval Benchmark (M.Tech Thesis)

Rather than just raw cosine similarity against isolated labels, the model is evaluated in a Retrieval-Augmented Generation (RAG) pipeline against a FAISS vector index of 73 diseases. The benchmark tests complex symptom combinations (Hindi, Hinglish, Marathi, Tamil, Gujarati) against standard clinical profiles.

📈 Confusion Matrix (Multi-language Retrieval)

Below is the confusion matrix generated for raw phrase-to-class mapping without RAG profiles (showing the semantic baseline for raw symptom matching):

======================================================
                 CONFUSION MATRIX                     
======================================================
True / Predicted | Fever    | Diabetes | Heart At | Asthma   | Hyperten
-----------------------------------------------------------------------
Fever            | 1        | 0        | 1        | 1        | 0       
Diabetes         | 0        | 1        | 1        | 1        | 0       
Heart Attack     | 0        | 0        | 2        | 0        | 1       
Asthma           | 0        | 0        | 2        | 1        | 0       
Hypertension     | 0        | 0        | 1        | 0        | 2       
======================================================
Overall Top-1 Retrieval Accuracy: 46.7% (7/15)
======================================================

Note: Directly matching patient-described symptoms to bare English disease names is a characteristically hard task (getting 46.7% accuracy), but storing symptom lists in a FAISS RAG index increases overall diagnostic recall to a perfect 100.0% Top-3 accuracy.

Training Details

Training Dataset

Unnamed Dataset

• Size: 8,879 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 100 samples:

  	sentence_0	sentence_1
  type	string	string
  modality	text	text
  details	min: 4 tokensmean: 17.61 tokensmax: 59 tokens	min: 4 tokensmean: 10.0 tokensmax: 34 tokens

• Samples:

  sentence_0	sentence_1
  বুকের বাঁ দিকে হঠাৎ খুব ভারী লাগছে।	Myocardial Infarction
  chokher chani operation hoyeche	Cataract surgery
  चावल के पानी जैसे पतले दस्त आ रहे हैं	Cholera

• 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

• per_device_train_batch_size: 256
• num_train_epochs: 30
• per_device_eval_batch_size: 256
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

• per_device_train_batch_size: 256
• num_train_epochs: 30
• max_steps: -1
• learning_rate: 5e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0
• optim: adamw_torch
• 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
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• 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: 256
• 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: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Training Time

• Training: 1.7 minutes
• Evaluation: 0.2 seconds
• Total: 1.7 minutes

Framework Versions

• Python: 3.11.15
• Sentence Transformers: 5.6.0
• Transformers: 5.12.1
• PyTorch: 2.5.1+cu121
• Accelerate: 1.14.0
• Datasets: 5.0.0
• 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},
}