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.

python

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.

Metric	Local Laptop CPU	Description
Precision @ 1	`97.3%`	The correct clinical disease was the absolute #1 retrieved profile.
Precision @ 3	`100.0%`	The correct disease was within the top 3 retrieved profiles.
Avg Retrieval Latency	`225.4 ms`	Fast sub-second local retrieval via FAISS.

📈 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 tokens
mean: 17.61 tokens
max: 59 tokens
min: 4 tokens
mean: 10.0 tokens
max: 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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

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

Click to expand

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

Epoch	Step	spearman_cosine
1.0	35	0.2122
2.0	70	0.3213
3.0	105	0.3320
4.0	140	0.3404
5.0	175	0.3568
5.7143	200	0.3697
6.0	210	0.3790
7.0	245	0.3877
8.0	280	0.3958
9.0	315	0.4021

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

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