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---
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](https://www.SBERT.net) model finetuned from [medicalai/ClinicalBERT](https://huggingface.co/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](https://huggingface.co/medicalai/ClinicalBERT) <!-- at revision 3bb5faa9f33458dd7801549e88767c3b23264942 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- parquet
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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("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]
```
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## Evaluation
### Metrics
#### Triplet
* Datasets: `ai-job-validation` and `ai-job-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | ai-job-validation | ai-job-test |
|:--------------------|:------------------|:------------|
| **cosine_accuracy** | **0.9429** | **0.9291** |
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## Training Details
### Training Dataset
#### parquet
* Dataset: parquet
* Size: 7,999 training samples
* Columns: <code>Entities</code>, <code>PosLongDesc</code>, and <code>NegLongDesc</code>
* Approximate statistics based on the first 1000 samples:
| | Entities | PosLongDesc | NegLongDesc |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 155.39 tokens</li><li>max: 485 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.35 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
| Entities | PosLongDesc | NegLongDesc |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
| <code>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</code> | <code>Rash and other nonspecific skin eruption</code> | <code>Acute nasopharyngitis [common cold]</code> |
| <code>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</code> | <code>Epileptic seizures related to external causes, not intractable, without status epilepticus</code> | <code>COVID-19</code> |
| <code>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</code> | <code>Pain in throat</code> | <code>Encounter for observation for suspected exposure to other biological agents ruled out</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### parquet
* Dataset: parquet
* Size: 999 evaluation samples
* Columns: <code>Entities</code>, <code>PosLongDesc</code>, and <code>NegLongDesc</code>
* Approximate statistics based on the first 999 samples:
| | Entities | PosLongDesc | NegLongDesc |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 154.58 tokens</li><li>max: 470 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.61 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.12 tokens</li><li>max: 35 tokens</li></ul> |
* Samples:
| Entities | PosLongDesc | NegLongDesc |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------|:------------------------------------------------------------|
| <code>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</code> | <code>Acute atopic conjunctivitis, bilateral</code> | <code>Deficiency of other specified B group vitamins</code> |
| <code>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</code> | <code>Local infection of the skin and subcutaneous tissue, unspecified</code> | <code>Inflammatory polyarthropathy</code> |
| <code>respiratory rate,sp,pain,sy,lic,bp,mm,dia,bp,mm,height,weight,00 kg,repeat,prescription</code> | <code>Menopausal and female climacteric states</code> | <code>COVID-19</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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
</details>
### 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
```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{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}
}
```
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