metadata

library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:7999
  - loss:MultipleNegativesRankingLoss
base_model: medicalai/ClinicalBERT
metrics:
  - cosine_accuracy
widget:
  - source_sentence: >-
      pt,dressing,pi,surgery,2 weeks,o,ozing,regular,dressing,weight,111.
      800,height,179. 000,temperature,97. 700,pulse,88. 000,res,19. 000
      bp,sy,sto,145. 000 bp,dia,sto,82. 000 spo,2,:,99,cap,blood
      sugar,ja,undice,ec,no past medical history,no past medical history,no past
      medical history,no past medical history,no past medical history,no past
      medical history,no past,no,no,no
      past,no,past,no,no,no,no,no,no,no,no,no,no,no,stable,stable,stable,stable,stable,stable,stable,stable,stable,stable,normal,no,surgical
      history,no,surgical history,no,surgical history,no,no
    sentences:
      - Acne vulgaris
      - Encounter for change or removal of surgical wound dressing
      - Irritant contact dermatitis due to detergents
  - source_sentence: >-
      fa,dubai,arab emirates,cardiac
      assessment,chest,pain,nausea,mild,dizzy,sleep,clinic,pulse,70,res,18,res,normal,sao,:,98,air
      time,00 : 39 : 00,bp,140 / 100,cap,< 2 sec,temperature,36,>,3
      reacts,right,>,3 reacts,total,gcs,15,car,mild
    sentences:
      - Dizziness and giddiness
      - Pruritus, unspecified
      - Acute gastritis without bleeding
  - source_sentence: >-
      low,back,pain,1,no,sp,fine,lower back,moderate,1
      month,no,diseases,no,no,no,no,no,no,single,normal,no,no,no,normal,normal,normal,normal,cvs,cv,normal,abnormal,-
      genito - urinary,normal,systems _ cns -
      cns,normal,musc,mu,normal,ps,normal,systems,endo -
      end,normal,normal,haemo,haem,normal,low,back,pain,1 month
    sentences:
      - Headache
      - Muscle spasm of back
      - Other chest pain
  - source_sentence: >-
      fa,ap,arab,mobility,knee
      assessment,ambula,tory,c,/,o,pain,swelling,right,cold
      pack,crepebanda,v,pt,transfer,pulse,68r,16,res,normal,sao,: 100,air
      time,07 : 29 : 00,bp,112 / 78,cap,< 2 sec,4
      reacts,right,-,>,3,reacts,gcs,15,pain,4,blood,car accident,twisted,right
      ankle
    sentences:
      - Unspecified injury of right ankle, initial encounter
      - Other spondylosis with radiculopathy, lumbosacral region
      - Right upper quadrant pain
  - source_sentence: >-
      irregular,period,few months,moderate,few months ago,none,weight,90.
      000,height,163. 000,temperature,98. 600,pulse,82. 000,respiration,19. 000
      bp,systolic,110. 000 bp,diastolic,70. 000,sp,o,2,:,99,cap,blood
      sugar,ja,und,ice,ec,abd,an,l,girth,head,chest,ch ida ch vitamin d
      deficiency,polycystic ovary syndrome,ch ida ch vitamin d
      deficiency,polycystic ovary syndrome,ch,ida ch vitamin d
      deficiency,polycystic ovary syndrome,ch,ida ch vitamin d
      deficiency,polycystic ovary syndrome,no,no family,no,no,nation,grade
      11,grade 11,grade 11,grade 11,no,no,no,no,normal,normal,normal,normal,_
      cvs,cv,normal,normal,irregular
      period,cns,cn,normal,mu,normal,normal,normal,normal,normal,normal,irregular
      period
    sentences:
      - Pain in right hip
      - Radial styloid tenosynovitis [de Quervain]
      - Irregular menstruation, unspecified
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on medicalai/ClinicalBERT
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: ai job validation
          type: ai-job-validation
        metrics:
          - type: cosine_accuracy
            value: 0.9429429173469543
            name: Cosine Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: ai job test
          type: ai-job-test
        metrics:
          - type: cosine_accuracy
            value: 0.9290709495544434
            name: Cosine Accuracy

SentenceTransformer based on medicalai/ClinicalBERT

This is a sentence-transformers model finetuned from medicalai/ClinicalBERT on the parquet dataset. 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: medicalai/ClinicalBERT
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- parquet

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: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("khaled-omar/distilroberta-ai-job-embeddings")
# Run inference
sentences = [
    'irregular,period,few months,moderate,few months ago,none,weight,90. 000,height,163. 000,temperature,98. 600,pulse,82. 000,respiration,19. 000 bp,systolic,110. 000 bp,diastolic,70. 000,sp,o,2,:,99,cap,blood sugar,ja,und,ice,ec,abd,an,l,girth,head,chest,ch ida ch vitamin d deficiency,polycystic ovary syndrome,ch ida ch vitamin d deficiency,polycystic ovary syndrome,ch,ida ch vitamin d deficiency,polycystic ovary syndrome,ch,ida ch vitamin d deficiency,polycystic ovary syndrome,no,no family,no,no,nation,grade 11,grade 11,grade 11,grade 11,no,no,no,no,normal,normal,normal,normal,_ cvs,cv,normal,normal,irregular period,cns,cn,normal,mu,normal,normal,normal,normal,normal,normal,irregular period',
    'Irregular menstruation, unspecified',
    'Radial styloid tenosynovitis [de Quervain]',
]
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]

Evaluation

Metrics

Triplet

Datasets: ai-job-validation and ai-job-test
Evaluated with TripletEvaluator

Metric	ai-job-validation	ai-job-test
cosine_accuracy	0.9429	0.9291

Training Details

Training Dataset

parquet

Dataset: parquet
Size: 7,999 training samples
Columns: Entities, PosLongDesc, and NegLongDesc

Approximate statistics based on the first 1000 samples:

	Entities	PosLongDesc	NegLongDesc
type	string	string	string
details	min: 3 tokens mean: 155.39 tokens max: 485 tokens	min: 4 tokens mean: 10.62 tokens max: 32 tokens	min: 3 tokens mean: 10.35 tokens max: 31 tokens

Samples:

Entities	PosLongDesc	NegLongDesc
it,chiness,since 3 months,it,chiness,since,3 months,weight,90. 100,height,178. 000,temperature,98. 060,pulse,84. 000,respiration,0. 000 bp,sy,sto,122. 000 bp,dia,69. 000,sp,o,:,99,cap,blood sugar,ja,undice,ec,abd,an,rth,nonsignificant,nonsignificant,nonsignifican,t,no family,nonsignificant family,nonsignificant family,nonsignificant,no relevant family history,yes,married, smoker, carpenter,married, smoker, carpenter social,married, smoker, carpenter social history,nonsignificant,nonsignificant,nonsignificant,it,chiness,3 months,treatment	`Rash and other nonspecific skin eruption`	`Acute nasopharyngitis [common cold]`
`amc,dubai,united arab emirates,uma,pa,gut,hari,val,electrocard,gram,pt,amc,sitting,coherent,w /,can,nula,bra,chia,vital,85,18,res,normal,sao,100,air time,17,: 51 : 34,bp,120 / 81,cap,<,2,sec,temperature,> 4 reacts,>,4,reacts,total,gcs,15,pain,0,blood glucose,102,car,accident,drug overdose,intentional`	`Epileptic seizures related to external causes, not intractable, without status epilepticus`	`COVID-19`
`amc gate,dubai,united arab emirates,ssi,test,airports,dubai,concourse,ent assessment,throat,transported,endorsed,pulse :,77r,14,res,normal %,sao,2 :,100,air time,05 :,26,:,00,bp,118 / 69,cap,<,2,sec,temperature,36. 7,pupil,left,>,4,reacts,right,>,4,reacts,gcs,15,pain,2,blood glucose,96,car,accident,no,throatpain`	`Pain in throat`	`Encounter for observation for suspected exposure to other biological agents ruled out`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

parquet

Dataset: parquet
Size: 999 evaluation samples
Columns: Entities, PosLongDesc, and NegLongDesc

Approximate statistics based on the first 999 samples:

	Entities	PosLongDesc	NegLongDesc
type	string	string	string
details	min: 4 tokens mean: 154.58 tokens max: 470 tokens	min: 4 tokens mean: 10.61 tokens max: 31 tokens	min: 4 tokens mean: 10.12 tokens max: 35 tokens

Samples:

Entities	PosLongDesc	NegLongDesc
it,chy,redness,3 days,both,it,ching,mild,moderate,3 days,weight,50. 200,height,143. 000,temperature,98. 240,pulse,78. 000,respiration,0. 000 bp,systolic,0. 000 bp,dia,sto,lic,0. 000,sp,o,2,:,99,cap,blood sugar,ja,undice,ec,abd,no past medical history,no past medical history,unknown family medical history,negative family,chronic disease,no diabetic mellitus,no hypertention,negative family,chronic disease,no diabetic mellitus,no hypertention,no,7 years and,7 months,7 years,7 months,no,removal,int,removal,int,red,it,chy,it,chy,redness,3 days	`Acute atopic conjunctivitis, bilateral`	`Deficiency of other specified B group vitamins`
`pi,mples,pustules,plus,minus,cyst,both side,of the face,too,it,ching,skin,4,pi,notice,increase,laser removal,facial,expose,sun,pust,cyst,it,weight,52,.,800,height,159. 000,temperature,98. 100,pulse,93. 000,res,0. 000 bp,sy,sto,99. 000 bp,sto,60. 000,sp,o,98,cap,blood sugar,ja,undice,ec,no,no,ro,course,ro,not,course,no diabetic mellitus,no,les,no diabetic,mellit,us,no,les,basic,nation,nation,13,years,months,15 years,11 months,old,pu,ules,plus,cyst,side`	`Local infection of the skin and subcutaneous tissue, unspecified`	`Inflammatory polyarthropathy`
`respiratory rate,sp,pain,sy,lic,bp,mm,dia,bp,mm,height,weight,00 kg,repeat,prescription`	`Menopausal and female climacteric states`	`COVID-19`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1
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: False
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: False
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: False
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
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	ai-job-validation_cosine_accuracy	ai-job-test_cosine_accuracy
-1	-1	-	-	0.5495	-
0.2	100	2.8729	1.8172	0.8789	-
0.4	200	2.085	1.4398	0.9259	-
0.6	300	1.8233	1.3448	0.9339	-
0.8	400	1.6871	1.2579	0.9409	-
1.0	500	1.4881	1.2327	0.9429	-
-1	-1	-	-	0.9429	0.9291

Framework Versions

Python: 3.11.4
Sentence Transformers: 3.4.1
Transformers: 4.41.2
PyTorch: 2.3.1+cpu
Accelerate: 1.3.0
Datasets: 3.2.0
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}
}