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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:95159
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/multi-qa-mpnet-base-dot-v1
datasets: []
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
widget:
- source_sentence: medial deviation, first metatarsal misalignment
sentences:
- "Deviation of the first toe away from the rest of the foot\n\nHallux varus \n\
Other namesSandal gap[1] \nRadiography of the left foot of a young male showing\
\ progressive hallux varus \nSpecialtyOrthopedic \n \nHallux varus is a deformity\
\ of the great toe joint where the hallux (great toe) is deviated medially (towards\
\ the midline of the body) away from the first metatarsal bone. The hallux usually\
\ moves in the transverse plane. Unlike hallux valgus, also known as hallux abducto\
\ valgus or bunion, hallux varus is uncommon in the West but it is common in cultures\
\ where the population remains unshod.\n\n## Photos[edit]\n\n * \n\n## References[edit]\n\
\n 1. ^ Weerakkody, Yuranga. \"Sandal gap deformity - Radiology Reference Article\
\ - Radiopaedia.org\". radiopaedia.org.\n\n## External links[edit]\n\nClassification\n\
\nD\n\n * ICD-10: M20.3, Q66.3\n * ICD-9-CM: 735.1, 755.66\n * MeSH: D050488\n\
\n \n \n * v\n * t\n * e\n\nAcquired musculoskeletal deformities \n \n\
Upper limb\n\nshoulder"
- "Touraine (1955), who first described this condition (Touraine, 1941), discovered\
\ a total of 32 cases in 17 families examined. In 9 of the families, a parent\
\ and 1 or more children were affected. In 5 families with a total of 15 cases,\
\ only 2 or more sibs were affected. He quoted an instance of affected mother\
\ and 4 children. Mental retardation was frequently associated. In a series of\
\ 40 reported cases reviewed by Dociu et al. (1976), no lentigines were found\
\ other than those on the face.\n\n \nInheritance \\- Autosomal dominant Neuro\
\ \\- Mental retardation Skin \\- Facial lentigines ▲ Close"
- 'Trisomy 18, also called Edwards syndrome, is a chromosomal condition associated
with abnormalities in many parts of the body. Individuals with trisomy 18 often
have slow growth before birth (intrauterine growth retardation) and a low birth
weight. Affected individuals may have heart defects and abnormalities of other
organs that develop before birth. Other features of trisomy 18 include a small,
abnormally shaped head; a small jaw and mouth; and clenched fists with overlapping
fingers. Due to the presence of several life-threatening medical problems, many
individuals with trisomy 18 die before birth or within their first month. Five
to 10 percent of children with this condition live past their first year, and
these children often have severe intellectual disability.
## Frequency'
- source_sentence: hyperreflexia, infantile onset
sentences:
- 'Furuncular myiasis in humans is caused by two species: the Cayor worm (larvae
of the African tumbu fly Cordylobia anthropophaga) and the larvae of the human
botfly (Dermatobia hominis).
## Epidemiology
The prevalence is unknown but the cases reported in Europe occur following visits
to affected regions (Latin America, Sub-Saharan Africa) or in association with
animal importation.
## Clinical description
In the case of Cordylobia anthropophaga, the females lay their eggs on damp fabric
or on the ground. The larvae penetrate the skin following contact with the ground
or with non-ironed contaminated fabric. Infection becomes evident within 10 to
15 days with the formation of a pseudo-furuncle or emergence of a maggot. Dermatobia
hominis is found in Latin America. Infestation is usually localised to the scalp
of infected individuals.'
- A rare ARX-related epileptic encephalopathy characterized by infantile onset of
myoclonic epilepsy with generalized spasticity, severe global developmental delay,
and moderate to profound intellectual disability. Obligate female carriers show
subtle, generalized hyperreflexia. Late onset progressive spastic ataxia has also
been reported.
- Intestinal lymphangiectasia is a rare digestive disorder characterized by abnormally
enlarged lymph vessels supplying the lining of the small intestine. Affected people
may experience intermittent diarrhea, nausea, vomiting, swelling of the limbs
and abdominal discomfort. Intestinal lymphangiectasia can be congenital (also
called primary intestinal lymphangiectasia or Waldmann disease) in which case
it affects children and young adults (mean age of onset, 11 years); it can also
be associated with a variety of other conditions and affect older adults. Treatment
generally involves control of symptoms with dietary and/or behavioral modifications
and the use of certain medications.
- source_sentence: mutations in CYLD gene, chromosome 16q12-q13
sentences:
- '## Description
Multiple familial trichoepithelioma (MFT) is an autosomal dominant disorder of
skin appendage tumors characterized by the appearance of trichoepitheliomas.
See also MFT1 (601606), which is caused by mutations in the CYLD gene (605018)
on chromosome 16q12-q13.
Mapping
In 3 families with multiple familial trichoepithelioma, 2 African American and
1 Caucasian, Harada et al. (1996) found linkage of the disorder to a 4-cM region
between IFNA (147660) and D9S126 on chromosome 9p21; maximum combined lod = 3.31
at D9S171 at theta = 0.0.'
- This article has multiple issues. Please help improve it or discuss these issues
on the talk page. (Learn how and when to remove these template messages)
- 'Male congenital condition
Buried penis on a circumcised 30 year old male not due to obesity
Buried penis in a circumcised 40 year old male due to obesity
Buried penis (also known as hidden penis or retractile penis) is a congenital
or acquired condition, in which the penis is partially or completely hidden below
the surface of the skin. It was first described by Edward Lawrence Keyes in 1919
as the apparent absence of the penis and as being buried beneath the skin of the
abdomen, thigh, or scrotum.[1] Further research was done by Maurice Campbell in
1951 when he reported on the penis being buried beneath subcutaneous fat of the
scrotum, perineum, hypogastrium, and thigh.[2]
A buried penis can lead to obstruction of urinary stream, poor hygiene, soft tissue
infection, phimosis, and inhibition of normal sexual function.'
- source_sentence: metastasis, lung pain, liver symptoms
sentences:
- 'Testicular seminomatous germ cell tumor is a rare testicular germ cell tumor
(see this term), most commonly presenting with a painless mass in the scrotum,
with a very high cure rate if caught in the early stages.
## Epidemiology
Annual incidence in Europe is 1/62,000 people. It accounts for 40% of testicular
cancer cases.
## Clinical description
Seminoma usually presents in males between the ages of 30-40. A painless mass
in the scrotum is indicative of disease. A long-standing hydrocele may be noted
causing a feeling of heaviness in the testicle. Gynecomastia and back and flank
pain are symptoms that are seen in some patients. Relapse after surgery can occur,
usually (in 97% of cases) in the high iliac or retroperitoneal lymph nodes. Metastasis,
although rare, can occur in some cases, affecting the lungs, liver, bones and
central nervous system.
## Etiology'
- 'Proteus-like syndrome describes patients who do not meet the diagnostic criteria
for Proteus syndrome (see this term) but who share a multitude of characteristic
clinical features of the disease.
## Epidemiology
The prevalence is unknown.
## Clinical description
Proteus-like syndrome has the clinical features of Proteus syndrome but lacks
some of the required criteria necessary for diagnosis. The main clinical features
include skeletal overgrowth, hamartomous overgrowth of multiple tissues, cerebriform
connective tissue nevi, vascular malformations and linear epidermal nevi.
## Etiology'
- "\"ESUS\" redirects here. For other uses, see ESUS (disambiguation).\n\nEmbolic\
\ stroke of undetermined source (ESUS) is a type of ischemic stroke with an unknown\
\ origin, defined as a non-lacunar brain infarct without proximal arterial stenosis\
\ or cardioembolic sources.[1] As such, it forms a subset of cryptogenic stroke,\
\ which is part of the TOAST-classification.[2] The following diagnostic criteria\
\ define an ESUS:[1]\n\n * Stroke detected by CT or MRI that is not lacunar\n\
\ * No major-risk cardioembolic source of embolism\n * Absence of extracranial\
\ or intracranial atherosclerosis causing 50% luminal stenosis in arteries supplying\
\ the area of ischaemia\n * No other specific cause of stroke identified (e.g.,\
\ arteritis, dissection, migraine/vasospasm, drug misuse)\n\n## Contents\n\n \
\ * 1 Causes\n * 2 Diagnosis\n * 2.1 Cryptogenic stroke vs ESUS\n * 3 Management\n\
\ * 4 Epidemiology\n * 5 References\n * 6 Further reading\n\n## Causes[edit]"
- source_sentence: nerve cell dysfunction, riboflavin deficiency
sentences:
- Riboflavin transporter deficiency neuronopathy is a disorder that affects nerve
cells (neurons). Affected individuals typically have hearing loss caused by nerve
damage in the inner ear (sensorineural hearing loss) and signs of damage to other
nerves.
- 'A number sign (#) is used with this entry because autosomal recessive deafness-23
(DFNB23) is caused by homozygous mutation in the gene encoding protocadherin-15
(PCDH15; 605514) on chromosome 10q21.
Mutation in the PCDH15 gene can also cause Usher syndrome type IF (602083).
Clinical Features
Ahmed et al. (2003) reported 3 families with isolated deafness. Two of the families
had no history of nyctalopia, and the funduscopy and electroretinograms were normal
in 2 older affected individuals from each family (age range, 13-44 years). Vestibular
responses were intact in affected individuals.'
- 'A number sign (#) is used with this entry because hyperprolinemia type I (HYRPRO1)
is caused by homozygous or compound heterozygous mutation in the proline dehydrogenase
gene (PRODH; 606810) on chromosome 22q11.
The PRODH gene falls within the region deleted in the 22q11 deletion syndrome,
including DiGeorge syndrome (188400) and velocardiofacial syndrome (192430).
Description
Phang et al. (2001) noted that prospective studies of HPI probands identified
through newborn screening as well as reports of several families have suggested
that it is a metabolic disorder not clearly associated with clinical manifestations.
Phang et al. (2001) concluded that HPI is a relatively benign condition in most
individuals under most circumstances. However, other reports have suggested that
some patients have a severe phenotype with neurologic manifestations, including
epilepsy and mental retardation (Jacquet et al., 2003).
### Genetic Heterogeneity of Hyperprolinemia'
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.1933234251743455
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5625928889905111
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7512289927975306
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8409740482451126
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1933234251743455
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.187530962996837
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15024579855950612
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08409740482451128
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1933234251743455
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5625928889905111
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7512289927975306
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8409740482451126
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5119882960837339
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.405861873730865
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4109594895459784
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.1949239739339202
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5672802103578369
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7570595632788385
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8415456728021036
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.1949239739339202
name: Dot Precision@1
- type: dot_precision@3
value: 0.18909340345261233
name: Dot Precision@3
- type: dot_precision@5
value: 0.1514119126557677
name: Dot Precision@5
- type: dot_precision@10
value: 0.08415456728021035
name: Dot Precision@10
- type: dot_recall@1
value: 0.1949239739339202
name: Dot Recall@1
- type: dot_recall@3
value: 0.5672802103578369
name: Dot Recall@3
- type: dot_recall@5
value: 0.7570595632788385
name: Dot Recall@5
- type: dot_recall@10
value: 0.8415456728021036
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5141471619143755
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.40838527858078216
name: Dot Mrr@10
- type: dot_map@100
value: 0.4135618156873651
name: Dot Map@100
---
# SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1). 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:** [sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1) <!-- at revision 3af7c6da5b3e1bea796ef6c97fe237538cbe6e7f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Dot Product
<!-- - **Training Dataset:** Unknown -->
<!-- - **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: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'nerve cell dysfunction, riboflavin deficiency',
'Riboflavin transporter deficiency neuronopathy is a disorder that affects nerve cells (neurons). Affected individuals typically have hearing loss caused by nerve damage in the inner ear (sensorineural hearing loss) and signs of damage to other nerves.',
'A number sign (#) is used with this entry because hyperprolinemia type I (HYRPRO1) is caused by homozygous or compound heterozygous mutation in the proline dehydrogenase gene (PRODH; 606810) on chromosome 22q11.\n\nThe PRODH gene falls within the region deleted in the 22q11 deletion syndrome, including DiGeorge syndrome (188400) and velocardiofacial syndrome (192430).\n\nDescription\n\nPhang et al. (2001) noted that prospective studies of HPI probands identified through newborn screening as well as reports of several families have suggested that it is a metabolic disorder not clearly associated with clinical manifestations. Phang et al. (2001) concluded that HPI is a relatively benign condition in most individuals under most circumstances. However, other reports have suggested that some patients have a severe phenotype with neurologic manifestations, including epilepsy and mental retardation (Jacquet et al., 2003).\n\n### Genetic Heterogeneity of Hyperprolinemia',
]
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1933 |
| cosine_accuracy@3 | 0.5626 |
| cosine_accuracy@5 | 0.7512 |
| cosine_accuracy@10 | 0.841 |
| cosine_precision@1 | 0.1933 |
| cosine_precision@3 | 0.1875 |
| cosine_precision@5 | 0.1502 |
| cosine_precision@10 | 0.0841 |
| cosine_recall@1 | 0.1933 |
| cosine_recall@3 | 0.5626 |
| cosine_recall@5 | 0.7512 |
| cosine_recall@10 | 0.841 |
| cosine_ndcg@10 | 0.512 |
| cosine_mrr@10 | 0.4059 |
| cosine_map@100 | 0.411 |
| dot_accuracy@1 | 0.1949 |
| dot_accuracy@3 | 0.5673 |
| dot_accuracy@5 | 0.7571 |
| dot_accuracy@10 | 0.8415 |
| dot_precision@1 | 0.1949 |
| dot_precision@3 | 0.1891 |
| dot_precision@5 | 0.1514 |
| dot_precision@10 | 0.0842 |
| dot_recall@1 | 0.1949 |
| dot_recall@3 | 0.5673 |
| dot_recall@5 | 0.7571 |
| dot_recall@10 | 0.8415 |
| dot_ndcg@10 | 0.5141 |
| dot_mrr@10 | 0.4084 |
| **dot_map@100** | **0.4136** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 95,159 training samples
* Columns: <code>queries</code> and <code>chunks</code>
* Approximate statistics based on the first 1000 samples:
| | queries | chunks |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.01 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 158.91 tokens</li><li>max: 319 tokens</li></ul> |
* Samples:
| queries | chunks |
|:-------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>hypotrichosis, wiry hair, onycholysis</code> | <code>Green et al. (2003) reported an Australian family in which 22 members over 4 generations had progressive patterned scalp hypotrichosis and wiry hair similar to that seen in Marie Unna hereditary hypotrichosis (MUHH; 146550). Features differing from those of MUHH included absence of signs of abnormality at birth, relative sparing of body hair, distal onycholysis, and intermittent cosegregation with autosomal dominant cleft lip and palate. Five individuals had associated cleft lip and palate. Green et al. (2003) excluded linkage of the disorder in the Australian family to the MUHH locus on chromosome 8p21.</code> |
| <code>cleft lip, cleft palate, hair loss</code> | <code>Green et al. (2003) reported an Australian family in which 22 members over 4 generations had progressive patterned scalp hypotrichosis and wiry hair similar to that seen in Marie Unna hereditary hypotrichosis (MUHH; 146550). Features differing from those of MUHH included absence of signs of abnormality at birth, relative sparing of body hair, distal onycholysis, and intermittent cosegregation with autosomal dominant cleft lip and palate. Five individuals had associated cleft lip and palate. Green et al. (2003) excluded linkage of the disorder in the Australian family to the MUHH locus on chromosome 8p21.</code> |
| <code>progressive patterned scalp, autosomal dominant inheritance</code> | <code>Green et al. (2003) reported an Australian family in which 22 members over 4 generations had progressive patterned scalp hypotrichosis and wiry hair similar to that seen in Marie Unna hereditary hypotrichosis (MUHH; 146550). Features differing from those of MUHH included absence of signs of abnormality at birth, relative sparing of body hair, distal onycholysis, and intermittent cosegregation with autosomal dominant cleft lip and palate. Five individuals had associated cleft lip and palate. Green et al. (2003) excluded linkage of the disorder in the Australian family to the MUHH locus on chromosome 8p21.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 1,
"similarity_fct": "dot_score"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 8,747 evaluation samples
* Columns: <code>queries</code> and <code>chunks</code>
* Approximate statistics based on the first 1000 samples:
| | queries | chunks |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.71 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 155.81 tokens</li><li>max: 305 tokens</li></ul> |
* Samples:
| queries | chunks |
|:-----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>white patches, corrugated tongue, immunocompromised, Epstein-Barr virus</code> | <code>Not to be confused with Hairy tongue.<br><br>Hairy leukoplakia <br>Other namesOral hairy leukoplakia,[1]:385 OHL, or HIV-associated hairy leukoplakia[2] <br>SpecialtyGastroenterology <br> <br>Hairy leukoplakia is a white patch on the side of the tongue with a corrugated or hairy appearance. It is caused by Epstein-Barr virus (EBV) and occurs usually in persons who are immunocompromised, especially those with human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS). The white lesion, which cannot be scraped off, is benign and does not require any treatment, although its appearance may have diagnostic and prognostic implications for the underlying condition.<br><br>Depending upon what definition of leukoplakia is used, hairy leukoplakia is sometimes considered a subtype of leukoplakia, or a distinct diagnosis.<br><br>## Contents</code> |
| <code>HIV-associated lesions, oral hairy leukoplakia, benign white lesions, tongue appearance</code> | <code>Not to be confused with Hairy tongue.<br><br>Hairy leukoplakia <br>Other namesOral hairy leukoplakia,[1]:385 OHL, or HIV-associated hairy leukoplakia[2] <br>SpecialtyGastroenterology <br> <br>Hairy leukoplakia is a white patch on the side of the tongue with a corrugated or hairy appearance. It is caused by Epstein-Barr virus (EBV) and occurs usually in persons who are immunocompromised, especially those with human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS). The white lesion, which cannot be scraped off, is benign and does not require any treatment, although its appearance may have diagnostic and prognostic implications for the underlying condition.<br><br>Depending upon what definition of leukoplakia is used, hairy leukoplakia is sometimes considered a subtype of leukoplakia, or a distinct diagnosis.<br><br>## Contents</code> |
| <code>hairy leukoplakia symptoms, non-scrapable lesions, HIV/AIDS, oral lesions</code> | <code>Not to be confused with Hairy tongue.<br><br>Hairy leukoplakia <br>Other namesOral hairy leukoplakia,[1]:385 OHL, or HIV-associated hairy leukoplakia[2] <br>SpecialtyGastroenterology <br> <br>Hairy leukoplakia is a white patch on the side of the tongue with a corrugated or hairy appearance. It is caused by Epstein-Barr virus (EBV) and occurs usually in persons who are immunocompromised, especially those with human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS). The white lesion, which cannot be scraped off, is benign and does not require any treatment, although its appearance may have diagnostic and prognostic implications for the underlying condition.<br><br>Depending upon what definition of leukoplakia is used, hairy leukoplakia is sometimes considered a subtype of leukoplakia, or a distinct diagnosis.<br><br>## Contents</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 1,
"similarity_fct": "dot_score"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 15
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `eval_on_start`: True
- `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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 15
- `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`: True
- `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`: True
- `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`: True
- `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
- `eval_on_start`: True
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | dot_map@100 |
|:-----------:|:--------:|:-------------:|:----------:|:-----------:|
| 0 | 0 | - | 1.4355 | 0.2271 |
| 0.1346 | 100 | 1.2599 | - | - |
| 0.2692 | 200 | 0.7627 | - | - |
| 0.4038 | 300 | 0.6061 | - | - |
| 0.5384 | 400 | 0.5632 | - | - |
| 0.6729 | 500 | 0.3965 | 0.4589 | 0.3852 |
| 0.8075 | 600 | 0.3104 | - | - |
| 0.9421 | 700 | 0.446 | - | - |
| 1.0767 | 800 | 0.4426 | - | - |
| 1.2113 | 900 | 0.4518 | - | - |
| 1.3459 | 1000 | 0.4145 | 0.3726 | 0.3964 |
| 1.4805 | 1100 | 0.4296 | - | - |
| 1.6151 | 1200 | 0.4144 | - | - |
| 1.7497 | 1300 | 0.1536 | - | - |
| 1.8843 | 1400 | 0.3425 | - | - |
| 2.0188 | 1500 | 0.3225 | 0.3433 | 0.3930 |
| 2.1534 | 1600 | 0.3529 | - | - |
| 2.2880 | 1700 | 0.3382 | - | - |
| 2.4226 | 1800 | 0.3092 | - | - |
| 2.5572 | 1900 | 0.339 | - | - |
| 2.6918 | 2000 | 0.1681 | 0.3633 | 0.4032 |
| 2.8264 | 2100 | 0.1753 | - | - |
| 2.9610 | 2200 | 0.2552 | - | - |
| 3.0956 | 2300 | 0.2549 | - | - |
| 3.2301 | 2400 | 0.2759 | - | - |
| 3.3647 | 2500 | 0.2513 | 0.3338 | 0.4066 |
| 3.4993 | 2600 | 0.258 | - | - |
| 3.6339 | 2700 | 0.2222 | - | - |
| 3.7685 | 2800 | 0.0541 | - | - |
| 3.9031 | 2900 | 0.2275 | - | - |
| 4.0377 | 3000 | 0.1919 | 0.3529 | 0.4026 |
| 4.1723 | 3100 | 0.215 | - | - |
| 4.3069 | 3200 | 0.2114 | - | - |
| 4.4415 | 3300 | 0.2153 | - | - |
| 4.5760 | 3400 | 0.2164 | - | - |
| 4.7106 | 3500 | 0.0773 | 0.3509 | 0.4090 |
| 4.8452 | 3600 | 0.1211 | - | - |
| 4.9798 | 3700 | 0.1553 | - | - |
| 5.1144 | 3800 | 0.1764 | - | - |
| 5.2490 | 3900 | 0.1953 | - | - |
| 5.3836 | 4000 | 0.1559 | 0.3474 | 0.4089 |
| 5.5182 | 4100 | 0.1686 | - | - |
| 5.6528 | 4200 | 0.1327 | - | - |
| 5.7873 | 4300 | 0.0514 | - | - |
| 5.9219 | 4400 | 0.1381 | - | - |
| 6.0565 | 4500 | 0.1445 | 0.3521 | 0.4056 |
| 6.1911 | 4600 | 0.1621 | - | - |
| 6.3257 | 4700 | 0.1365 | - | - |
| 6.4603 | 4800 | 0.1579 | - | - |
| 6.5949 | 4900 | 0.1547 | - | - |
| 6.7295 | 5000 | 0.0316 | 0.3895 | 0.4094 |
| 6.8641 | 5100 | 0.0958 | - | - |
| 6.9987 | 5200 | 0.1082 | - | - |
| 7.1332 | 5300 | 0.1379 | - | - |
| 7.2678 | 5400 | 0.1348 | - | - |
| 7.4024 | 5500 | 0.1322 | 0.3552 | 0.4100 |
| 7.5370 | 5600 | 0.1321 | - | - |
| 7.6716 | 5700 | 0.0763 | - | - |
| 7.8062 | 5800 | 0.0472 | - | - |
| 7.9408 | 5900 | 0.0989 | - | - |
| 8.0754 | 6000 | 0.1045 | 0.3631 | 0.3967 |
| 8.2100 | 6100 | 0.122 | - | - |
| 8.3445 | 6200 | 0.1057 | - | - |
| 8.4791 | 6300 | 0.1194 | - | - |
| 8.6137 | 6400 | 0.113 | - | - |
| 8.7483 | 6500 | 0.0126 | 0.3944 | 0.4116 |
| 8.8829 | 6600 | 0.089 | - | - |
| 9.0175 | 6700 | 0.0849 | - | - |
| 9.1521 | 6800 | 0.1052 | - | - |
| 9.2867 | 6900 | 0.111 | - | - |
| 9.4213 | 7000 | 0.1026 | 0.3665 | 0.4133 |
| 9.5559 | 7100 | 0.1165 | - | - |
| 9.6904 | 7200 | 0.0394 | - | - |
| 9.8250 | 7300 | 0.0443 | - | - |
| 9.9596 | 7400 | 0.0756 | - | - |
| 10.0942 | 7500 | 0.0806 | 0.3785 | 0.4090 |
| 10.2288 | 7600 | 0.103 | - | - |
| 10.3634 | 7700 | 0.0875 | - | - |
| 10.4980 | 7800 | 0.0959 | - | - |
| 10.6326 | 7900 | 0.0851 | - | - |
| **10.7672** | **8000** | **0.0073** | **0.3902** | **0.4136** |
| 10.9017 | 8100 | 0.079 | - | - |
| 11.0363 | 8200 | 0.0664 | - | - |
| 11.1709 | 8300 | 0.0766 | - | - |
| 11.3055 | 8400 | 0.084 | - | - |
| 11.4401 | 8500 | 0.0947 | 0.3733 | 0.4099 |
| 11.5747 | 8600 | 0.0906 | - | - |
| 11.7093 | 8700 | 0.0224 | - | - |
| 11.8439 | 8800 | 0.0424 | - | - |
| 11.9785 | 8900 | 0.0569 | - | - |
| 12.1131 | 9000 | 0.0697 | 0.3824 | 0.4071 |
| 12.2476 | 9100 | 0.095 | - | - |
| 12.3822 | 9200 | 0.0651 | - | - |
| 12.5168 | 9300 | 0.0756 | - | - |
| 12.6514 | 9400 | 0.065 | - | - |
| 12.7860 | 9500 | 0.0194 | 0.3876 | 0.4110 |
| 12.9206 | 9600 | 0.0595 | - | - |
| 13.0552 | 9700 | 0.0629 | - | - |
| 13.1898 | 9800 | 0.0808 | - | - |
| 13.3244 | 9900 | 0.0652 | - | - |
| 13.4590 | 10000 | 0.0802 | 0.3783 | 0.4091 |
| 13.5935 | 10100 | 0.0809 | - | - |
| 13.7281 | 10200 | 0.0111 | - | - |
| 13.8627 | 10300 | 0.0465 | - | - |
| 13.9973 | 10400 | 0.0504 | - | - |
| 14.1319 | 10500 | 0.068 | 0.3831 | 0.4071 |
| 14.2665 | 10600 | 0.0739 | - | - |
| 14.4011 | 10700 | 0.0734 | - | - |
| 14.5357 | 10800 | 0.0737 | - | - |
| 14.6703 | 10900 | 0.0379 | - | - |
| 14.8048 | 11000 | 0.0231 | 0.3841 | 0.4112 |
| 14.9394 | 11100 | 0.0493 | - | - |
| 15.0 | 11145 | - | 0.3902 | 0.4136 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.43.3
- PyTorch: 2.3.1+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.2
- 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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