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---
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library_name: sentence-transformers
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:7999
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- loss:MultipleNegativesRankingLoss
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base_model: medicalai/ClinicalBERT
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metrics:
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- cosine_accuracy
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widget:
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- source_sentence: pt,dressing,pi,surgery,2 weeks,o,ozing,regular,dressing,weight,111.
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800,height,179. 000,temperature,97. 700,pulse,88. 000,res,19. 000 bp,sy,sto,145.
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000 bp,dia,sto,82. 000 spo,2,:,99,cap,blood sugar,ja,undice,ec,no past medical
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history,no past medical history,no past medical history,no past medical history,no
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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
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history,no,surgical history,no,surgical history,no,no
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sentences:
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- Acne vulgaris
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- Encounter for change or removal of surgical wound dressing
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- Irritant contact dermatitis due to detergents
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- source_sentence: 'fa,dubai,arab emirates,cardiac assessment,chest,pain,nausea,mild,dizzy,sleep,clinic,pulse,70,res,18,res,normal,sao,:,98,air
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time,00 : 39 : 00,bp,140 / 100,cap,< 2 sec,temperature,36,>,3 reacts,right,>,3
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reacts,total,gcs,15,car,mild'
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sentences:
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- Dizziness and giddiness
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- Pruritus, unspecified
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- Acute gastritis without bleeding
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- 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,-
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genito - urinary,normal,systems _ cns - cns,normal,musc,mu,normal,ps,normal,systems,endo
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- end,normal,normal,haemo,haem,normal,low,back,pain,1 month
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sentences:
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- Headache
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- Muscle spasm of back
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- Other chest pain
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- source_sentence: 'fa,ap,arab,mobility,knee assessment,ambula,tory,c,/,o,pain,swelling,right,cold
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pack,crepebanda,v,pt,transfer,pulse,68r,16,res,normal,sao,: 100,air time,07 :
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29 : 00,bp,112 / 78,cap,< 2 sec,4 reacts,right,-,>,3,reacts,gcs,15,pain,4,blood,car
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accident,twisted,right ankle'
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sentences:
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- Unspecified injury of right ankle, initial encounter
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- Other spondylosis with radiculopathy, lumbosacral region
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- Right upper quadrant pain
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- source_sentence: irregular,period,few months,moderate,few months ago,none,weight,90.
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000,height,163. 000,temperature,98. 600,pulse,82. 000,respiration,19. 000 bp,systolic,110.
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000 bp,diastolic,70. 000,sp,o,2,:,99,cap,blood sugar,ja,und,ice,ec,abd,an,l,girth,head,chest,ch
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ida ch vitamin d deficiency,polycystic ovary syndrome,ch ida ch vitamin d deficiency,polycystic
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ovary syndrome,ch,ida ch vitamin d deficiency,polycystic ovary syndrome,ch,ida
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ch vitamin d deficiency,polycystic ovary syndrome,no,no family,no,no,nation,grade
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11,grade 11,grade 11,grade 11,no,no,no,no,normal,normal,normal,normal,_ cvs,cv,normal,normal,irregular
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period,cns,cn,normal,mu,normal,normal,normal,normal,normal,normal,irregular period
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sentences:
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- Pain in right hip
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- Radial styloid tenosynovitis [de Quervain]
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- Irregular menstruation, unspecified
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pipeline_tag: sentence-similarity
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model-index:
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- name: SentenceTransformer based on medicalai/ClinicalBERT
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results:
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- task:
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type: triplet
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name: Triplet
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dataset:
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name: ai job validation
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type: ai-job-validation
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metrics:
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- type: cosine_accuracy
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value: 0.9429429173469543
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name: Cosine Accuracy
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- task:
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type: triplet
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name: Triplet
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dataset:
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name: ai job test
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type: ai-job-test
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metrics:
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- type: cosine_accuracy
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value: 0.9290709495544434
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name: Cosine Accuracy
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---
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# SentenceTransformer based on medicalai/ClinicalBERT
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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.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) <!-- at revision 3bb5faa9f33458dd7801549e88767c3b23264942 -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 dimensions
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- parquet
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
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(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})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("khaled-omar/distilroberta-ai-job-embeddings")
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# Run inference
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sentences = [
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'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',
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'Irregular menstruation, unspecified',
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'Radial styloid tenosynovitis [de Quervain]',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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## Evaluation
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### Metrics
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#### Triplet
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* Datasets: `ai-job-validation` and `ai-job-test`
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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| Metric | ai-job-validation | ai-job-test |
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|:--------------------|:------------------|:------------|
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| **cosine_accuracy** | **0.9429** | **0.9291** |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Dataset
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#### parquet
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* Dataset: parquet
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* Size: 7,999 training samples
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* Columns: <code>Entities</code>, <code>PosLongDesc</code>, and <code>NegLongDesc</code>
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* Approximate statistics based on the first 1000 samples:
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| | Entities | PosLongDesc | NegLongDesc |
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|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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| type | string | string | string |
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| 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> |
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* Samples:
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| Entities | PosLongDesc | NegLongDesc |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
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| <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> |
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| <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> |
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| <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> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Evaluation Dataset
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#### parquet
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* Dataset: parquet
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* Size: 999 evaluation samples
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* Columns: <code>Entities</code>, <code>PosLongDesc</code>, and <code>NegLongDesc</code>
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* Approximate statistics based on the first 999 samples:
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| | Entities | PosLongDesc | NegLongDesc |
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|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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| type | string | string | string |
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| 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> |
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* Samples:
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| Entities | PosLongDesc | NegLongDesc |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------|:------------------------------------------------------------|
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| <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> |
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| <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> |
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| <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> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `learning_rate`: 2e-05
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- `num_train_epochs`: 1
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- `warmup_ratio`: 0.1
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- `batch_sampler`: no_duplicates
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `learning_rate`: 2e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`: 1
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 42
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: False
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- `fp16`: False
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: False
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- `hub_always_push`: False
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `dispatch_batches`: None
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `prompts`: None
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- `batch_sampler`: no_duplicates
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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| 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 |
|
|
|
|
|
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### 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
|
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## Citation
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|
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### BibTeX
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#### Sentence Transformers
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|
```bibtex
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|
@inproceedings{reimers-2019-sentence-bert,
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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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|
}
|
|
```
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