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retrieval-mpnet-dot-finetuned-llama3-openbiollm-synthetic-dataset

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:89218
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:89218
- 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: Pulmonary stenoses, brachytelephalangy, inner ear deafness
sentences:
- "This article needs more medical references for verification or relies too heavily\
\ on primary sources. Please review the contents of the article and add the appropriate\
\ references if you can. Unsourced or poorly sourced material may be challenged\
\ and removed. \nFind sources: \"Chondropathy\" – news · newspapers · books ·\
\ scholar · JSTOR (October 2020) \n \nChondropathy \nSpecialtyOrthopedics \
\ \n \nChondropathy refers to a disease of the cartilage. It is frequently divided\
\ into 5 grades, with 0-2 defined as normal and 3-4 defined as diseased.\n\n##\
\ Contents\n\n * 1 Some common diseases affecting/involving the cartilage\n \
\ * 2 Repairing articular cartilage damage\n * 3 References\n * 4 External links\n\
\n## Some common diseases affecting/involving the cartilage[edit]"
- 'A number sign (#) is used with this entry because of evidence that Keutel syndrome
(KTLS) is caused by homozygous mutation in the gene encoding the human matrix
Gla protein (MGP; 154870) on chromosome 12p12.
Description
Keutel syndrome is an autosomal recessive disorder characterized by multiple peripheral
pulmonary stenoses, brachytelephalangy, inner ear deafness, and abnormal cartilage
ossification or calcification (summary by Khosroshahi et al., 2014).
Clinical Features'
- '## Description
Primary or spontaneous detachment of the retina occurs due to underlying ocular
disease and often involves the vitreous as well as the retina. The precipitating
event is formation of a retinal tear or hole, which permits fluid to accumulate
under the sensory layers of the retina and creates an intraretinal cleavage that
destroys the neurosensory process of visual reception. Vitreoretinal degeneration
and tear formation are painless phenomena, and in most cases, significant vitreoretinal
pathology is found only after detachment of the retina starts to cause loss of
vision or visual field. Without surgical intervention, retinal detachment will
almost inevitably lead to total blindness (summary by McNiel and McPherson, 1971).
Clinical Features'
- source_sentence: APS, catastrophic, diagnostic criteria, treatment options
sentences:
- 'A number sign (#) is used with this entry because of evidence that myofibrillar
myopathy-8 (MFM8) is caused by homozygous or compound heterozygous mutation in
the PYROXD1 gene (617220) on chromosome 12p12.
Description
Myofibrillar myopathy-8 is an autosomal recessive myopathy characterized by childhood
onset of slowly progressive proximal muscle weakness and atrophy resulting in
increased falls, gait problems, and difficulty running or climbing stairs. Upper
and lower limbs are affected, and some individuals develop distal muscle weakness
and atrophy. Ambulation is generally preserved, and patients do not have significant
respiratory compromise. Muscle biopsy shows a mix of myopathic features, including
myofibrillar inclusions and sarcomeric disorganization (summary by O''Grady et
al., 2016).
For a general phenotypic description and a discussion of genetic heterogeneity
of myofibrillar myopathy, see MFM1 (601419).
Clinical Features'
- "Rectal tenesmus \nSpecialtyGeneral surgery \n \nRectal tenesmus is a feeling\
\ of incomplete defecation. It is the sensation of inability or difficulty to\
\ empty the bowel at defecation, even if the bowel contents have already been\
\ evacuated. Tenesmus indicates the feeling of a residue, and is not always correlated\
\ with the actual presence of residual fecal matter in the rectum. It is frequently\
\ painful and may be accompanied by involuntary straining and other gastrointestinal\
\ symptoms. Tenesmus has both a nociceptive and a neuropathic component.\n\nVesical\
\ tenesmus is a similar condition, experienced as a feeling of incomplete voiding\
\ despite the bladder being empty.\n\nOften, rectal tenesmus is simply called\
\ tenesmus. The term rectal tenesmus is a retronym to distinguish defecation-related\
\ tenesmus from vesical tenesmus.[1]"
- "This article needs additional citations for verification. Please help improve\
\ this article by adding citations to reliable sources. Unsourced material may\
\ be challenged and removed. \nFind sources: \"Catastrophic antiphospholipid\
\ syndrome\" – news · newspapers · books · scholar · JSTOR (February 2018) (Learn\
\ how and when to remove this template message) \n \nCatastrophic antiphospholipid\
\ syndrome \nOther namesCatastrophic APS"
- source_sentence: Excess cholesterol, foam cells, gallbladder wall changes
sentences:
- "Cholesterolosis of gallbladder \nMicrograph of cholesterolosis of the gallbladder,\
\ with an annotated foam cell. H&E stain. \nSpecialtyGastroenterology \n \n\
In surgical pathology, strawberry gallbladder, more formally cholesterolosis of\
\ the gallbladder and gallbladder cholesterolosis, is a change in the gallbladder\
\ wall due to excess cholesterol.[1]\n\nThe name strawberry gallbladder comes\
\ from the typically stippled appearance of the mucosal surface on gross examination,\
\ which resembles a strawberry. Cholesterolosis results from abnormal deposits\
\ of cholesterol esters in macrophages within the lamina propria (foam cells)\
\ and in mucosal epithelium. The gallbladder may be affected in a patchy localized\
\ form or in a diffuse form. The diffuse form macroscopically appears as a bright\
\ red mucosa with yellow mottling (due to lipid), hence the term strawberry gallbladder.\
\ It is not tied to cholelithiasis (gallstones) or cholecystitis (inflammation\
\ of the gallbladder).[2]\n\n## Contents"
- Meningococcal meningitis is an acute bacterial disease caused by Neisseria meningitides
that presents usually, but not always, with a rash (non blanching petechial or
purpuric rash), progressively developing signs of meningitis (fever, vomiting,
headache, photophobia, and neck stiffness) and later leading to confusion, delirium
and drowsiness. Neck stiffness and photophobia are often absent in infants and
young children who may manifest nonspecific signs such as irritability, inconsolable
crying, poor feeding, and a bulging fontanel. Meningococcal meningitis may also
present as part of early or late onset sepsis in neonates. The disease is potentially
fatal. Surviving patients may develop neurological sequelae that include sensorineural
hearing loss, seizures, spasticity, attention deficits and intellectual disability.
- "Retiform parapsoriasis \nSpecialtyDermatology \n \nRetiform parapsoriasis\
\ is a cutaneous condition, considered to be a type of large-plaque parapsoriasis.[1]\
\ It is characterized by widespread, ill-defined plaques on the skin, that have\
\ a net-like or zebra-striped pattern.[2] Skin atrophy, a wasting away of the\
\ cutaneous tissue, usually occurs within the area of these plaques.[1]\n\n##\
\ See also[edit]\n\n * Parapsoriasis\n * Poikiloderma vasculare atrophicans\n\
\ * List of cutaneous conditions\n\n## References[edit]\n\n 1. ^ a b Lambert\
\ WC, Everett MA (Oct 1981). \"The nosology of parapsoriasis\". J. Am. Acad. Dermatol.\
\ 5 (4): 373–95. doi:10.1016/S0190-9622(81)70100-2. PMID 7026622.\n 2. ^ Rapini,\
\ Ronald P.; Bolognia, Jean L.; Jorizzo, Joseph L. (2007). Dermatology: 2-Volume\
\ Set. St. Louis: Mosby. ISBN 1-4160-2999-0.\n\n## External links[edit]\n\nClassification\n\
\nD\n\n * ICD-10: L41.5\n * ICD-9-CM: 696.2\n\n \n \n * v\n * t\n * e\n\
\nPapulosquamous disorders \n \nPsoriasis\n\nPustular"
- source_sentence: Pulmonary hypoplasia, respiratory insufficiency, megaureter, hydronephrosis
sentences:
- 'A rare fetal lower urinary tract obstruction (LUTO) characterized by closure
or failure to develop an opening in the urethra and resulting in obstructive uropathy
presenting in utero as megacystis, oligohydramnios or anhydramnios, and potter
sequence.
## Epidemiology
Prevalence is unknown, but is higher in males than females.
## Clinical description
Atresia of urethra often presents on routine antenatal ultrasound with megacystis,
oligohydramnios or anhydramnios and sometimes urinary ascites. It may cause fetal
death. In cases that survive to birth, additional symptoms include respiratory
insufficiency due to pulmonary hypoplasia, megaureter, hydronephrosis and enlarged
often cystic and functionally impaired/non-functional dysplastic kidneys as well
as abdominal distention. Furthermore, a Potter sequence can be found due to oligo-
or anhydramnios. Patients may present with patent urachus or vesicocutaneous fistula.
## Etiology'
- X-linked distal spinal muscular atrophy type 3 is a rare distal hereditary motor
neuropathy characterized by slowly progressive atrophy and weakness of distal
muscles of hands and feet with normal deep tendon reflexes or absent ankle reflexes
and minimal or no sensory loss, sometimes mild proximal weakness in the legs and
feet and hand deformities in males.
- 'A number sign (#) is used with this entry because Chudley-McCullough syndrome
(CMCS) is caused by homozygous or compound heterozygous mutation in the GPSM2
gene (609245) on chromosome 1p13.
Description
Chudley-McCullough syndrome is an autosomal recessive neurologic disorder characterized
by early-onset sensorineural deafness and specific brain anomalies on MRI, including
hypoplasia of the corpus callosum, enlarged cysterna magna with mild focal cerebellar
dysplasia, and nodular heterotopia. Some patients have hydrocephalus. Psychomotor
development is normal (summary by Alrashdi et al., 2011).
Clinical Features'
- source_sentence: Thyroid-stimulating hormone receptor gene, chromosome 14q31, homozygous
mutation
sentences:
- 'A number sign (#) is used with this entry because dermatofibrosarcoma protuberans
is caused in most cases by a specific fusion of the COL1A1 gene (120150) with
the PDGFB gene (190040); see 190040.0002.
Description
Dermatofibrosarcoma protuberans (DFSP) is an uncommon, locally aggressive, but
rarely metastasizing tumor of the deep dermis and subcutaneous tissue. It typically
presents during early or middle adult life and is most frequently located on the
trunk and proximal extremities (Sandberg et al., 2003).
Clinical Features
DFSP was first described by Taylor (1890). Sirvent et al. (2003) stated that,
because DFSP is relatively rare, grows slowly, and has a low level of aggressiveness,
its clinical significance has been underestimated. In particular, they noted that
the existence of pediatric cases has been overlooked.
Gardner et al. (1998) described a father and son with dermatofibrosarcoma protuberans.
The tumors arose at ages 43 and 14 years, respectively.'
- "Visuospatial dysgnosia is a loss of the sense of \"whereness\" in the relation\
\ of oneself to one's environment and in the relation of objects to each other.[1]\
\ Visuospatial dysgnosia is often linked with topographical disorientation.\n\n\
## Contents\n\n * 1 Symptoms\n * 2 Lesion areas\n * 3 Case studies\n * 4 Therapies\n\
\ * 5 References\n\n## Symptoms[edit]\n\nThe syndrome rarely presents itself\
\ the same way in every patient. Some symptoms that occur may be:"
- 'A number sign (#) is used with this entry because of evidence that congenital
nongoitrous hypothyroidism-1 (CHNG1) is caused by homozygous or compound heterozygous
mutation in the gene encoding the thyroid-stimulating hormone receptor (TSHR;
603372) on chromosome 14q31.
Description
Resistance to thyroid-stimulating hormone (TSH; see 188540), a hallmark of congenital
nongoitrous hypothyroidism, causes increased levels of plasma TSH and low levels
of thyroid hormone. Only a subset of patients develop frank hypothyroidism; the
remainder are euthyroid and asymptomatic (so-called compensated hypothyroidism)
and are usually detected by neonatal screening programs (Paschke and Ludgate,
1997).
### Genetic Heterogeneity of Congenital Nongoitrous Hypothyroidism'
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: 0.1900990099009901
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5756875687568757
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7932893289328933
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8704070407040704
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1900990099009901
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19189585625229189
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15865786578657867
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08704070407040705
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1900990099009901
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5756875687568757
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7932893289328933
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8704070407040704
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.526584144074431
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.41522683220700946
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4194005014371134
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.188998899889989
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5761826182618262
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7954895489548955
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8710671067106711
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.188998899889989
name: Dot Precision@1
- type: dot_precision@3
value: 0.19206087275394204
name: Dot Precision@3
- type: dot_precision@5
value: 0.15909790979097907
name: Dot Precision@5
- type: dot_precision@10
value: 0.08710671067106711
name: Dot Precision@10
- type: dot_recall@1
value: 0.188998899889989
name: Dot Recall@1
- type: dot_recall@3
value: 0.5761826182618262
name: Dot Recall@3
- type: dot_recall@5
value: 0.7954895489548955
name: Dot Recall@5
- type: dot_recall@10
value: 0.8710671067106711
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5265923432373186
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4149802896956161
name: Dot Mrr@10
- type: dot_map@100
value: 0.41904239679820193
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 = [
'Thyroid-stimulating hormone receptor gene, chromosome 14q31, homozygous mutation',
'A number sign (#) is used with this entry because of evidence that congenital nongoitrous hypothyroidism-1 (CHNG1) is caused by homozygous or compound heterozygous mutation in the gene encoding the thyroid-stimulating hormone receptor (TSHR; 603372) on chromosome 14q31.\n\nDescription\n\nResistance to thyroid-stimulating hormone (TSH; see 188540), a hallmark of congenital nongoitrous hypothyroidism, causes increased levels of plasma TSH and low levels of thyroid hormone. Only a subset of patients develop frank hypothyroidism; the remainder are euthyroid and asymptomatic (so-called compensated hypothyroidism) and are usually detected by neonatal screening programs (Paschke and Ludgate, 1997).\n\n### Genetic Heterogeneity of Congenital Nongoitrous Hypothyroidism',
'Visuospatial dysgnosia is a loss of the sense of "whereness" in the relation of oneself to one\'s environment and in the relation of objects to each other.[1] Visuospatial dysgnosia is often linked with topographical disorientation.\n\n## Contents\n\n * 1 Symptoms\n * 2 Lesion areas\n * 3 Case studies\n * 4 Therapies\n * 5 References\n\n## Symptoms[edit]\n\nThe syndrome rarely presents itself the same way in every patient. Some symptoms that occur may be:',
]
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 | 0.1901 |
| cosine_accuracy@3 | 0.5757 |
| cosine_accuracy@5 | 0.7933 |
| cosine_accuracy@10 | 0.8704 |
| cosine_precision@1 | 0.1901 |
| cosine_precision@3 | 0.1919 |
| cosine_precision@5 | 0.1587 |
| cosine_precision@10 | 0.087 |
| cosine_recall@1 | 0.1901 |
| cosine_recall@3 | 0.5757 |
| cosine_recall@5 | 0.7933 |
| cosine_recall@10 | 0.8704 |
| cosine_ndcg@10 | 0.5266 |
| cosine_mrr@10 | 0.4152 |
| cosine_map@100 | 0.4194 |
| dot_accuracy@1 | 0.189 |
| dot_accuracy@3 | 0.5762 |
| dot_accuracy@5 | 0.7955 |
| dot_accuracy@10 | 0.8711 |
| dot_precision@1 | 0.189 |
| dot_precision@3 | 0.1921 |
| dot_precision@5 | 0.1591 |
| dot_precision@10 | 0.0871 |
| dot_recall@1 | 0.189 |
| dot_recall@3 | 0.5762 |
| dot_recall@5 | 0.7955 |
| dot_recall@10 | 0.8711 |
| dot_ndcg@10 | 0.5266 |
| dot_mrr@10 | 0.415 |
| **dot_map@100** | **0.419** |
<!--
## 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: 89,218 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: 7 tokens</li><li>mean: 18.07 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 161.59 tokens</li><li>max: 299 tokens</li></ul> |
* Samples:
| queries | chunks |
|:--------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Polyhydramnios, megalencephaly, symptomatic epilepsy</code> | <code>A number sign (#) is used with this entry because of evidence that polyhydramnios, megalencephaly, and symptomatic epilepsy (PMSE) is caused by homozygous mutation in the STRADA gene (608626) on chromosome 17q23.<br><br>Clinical Features</code> |
| <code>Polyhydramnios, megalencephaly, STRADA gene mutation</code> | <code>A number sign (#) is used with this entry because of evidence that polyhydramnios, megalencephaly, and symptomatic epilepsy (PMSE) is caused by homozygous mutation in the STRADA gene (608626) on chromosome 17q23.<br><br>Clinical Features</code> |
| <code>Megalencephaly, symptomatic epilepsy, chromosome 17q23</code> | <code>A number sign (#) is used with this entry because of evidence that polyhydramnios, megalencephaly, and symptomatic epilepsy (PMSE) is caused by homozygous mutation in the STRADA gene (608626) on chromosome 17q23.<br><br>Clinical Features</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: 18,180 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: 18.35 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 152.55 tokens</li><li>max: 312 tokens</li></ul> |
* Samples:
| queries | chunks |
|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Weight loss, anorexia, fatigue, epigastric pain and discomfort</code> | <code>Undifferentiated carcinoma of stomach is a rare epithelial tumour of the stomach that lacks any features of differentiation beyond an epithelial phenotype. The presenting symptoms are usually vague and nonspecific, such as weight loss, anorexia, fatigue, epigastric pain and discomfort, heartburn and nausea, vomiting or hematemesis. Patients may also be asymptomatic. Ascites, jaundice, intestinal obstruction and peripheral lymphadenopathy indicate advanced stages and metastatic spread.</code> |
| <code>Heartburn, nausea, vomiting, hematemesis</code> | <code>Undifferentiated carcinoma of stomach is a rare epithelial tumour of the stomach that lacks any features of differentiation beyond an epithelial phenotype. The presenting symptoms are usually vague and nonspecific, such as weight loss, anorexia, fatigue, epigastric pain and discomfort, heartburn and nausea, vomiting or hematemesis. Patients may also be asymptomatic. Ascites, jaundice, intestinal obstruction and peripheral lymphadenopathy indicate advanced stages and metastatic spread.</code> |
| <code>Ascites, jaundice, intestinal obstruction, peripheral lymphadenopathy</code> | <code>Undifferentiated carcinoma of stomach is a rare epithelial tumour of the stomach that lacks any features of differentiation beyond an epithelial phenotype. The presenting symptoms are usually vague and nonspecific, such as weight loss, anorexia, fatigue, epigastric pain and discomfort, heartburn and nausea, vomiting or hematemesis. Patients may also be asymptomatic. Ascites, jaundice, intestinal obstruction and peripheral lymphadenopathy indicate advanced stages and metastatic spread.</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`: 50
- `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`: 50
- `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 | - | 1.1605 | 0.2419 |
| 0.1435 | 100 | 1.2016 | - | - |
| 0.2869 | 200 | 0.7627 | - | - |
| 0.4304 | 300 | 0.5559 | - | - |
| 0.5739 | 400 | 0.4541 | - | - |
| 0.7174 | 500 | 0.1451 | 0.3600 | 0.3913 |
| 0.8608 | 600 | 0.3841 | - | - |
| 1.0057 | 700 | 0.3334 | - | - |
| 1.1492 | 800 | 0.3898 | - | - |
| 1.2927 | 900 | 0.3576 | - | - |
| 1.4362 | 1000 | 0.3563 | 0.2719 | 0.4127 |
| 1.5796 | 1100 | 0.3186 | - | - |
| 1.7231 | 1200 | 0.098 | - | - |
| 1.8666 | 1300 | 0.3038 | - | - |
| 2.0115 | 1400 | 0.2629 | - | - |
| 2.1549 | 1500 | 0.3221 | 0.2579 | 0.4155 |
| 2.2984 | 1600 | 0.2936 | - | - |
| 2.4419 | 1700 | 0.2867 | - | - |
| 2.5854 | 1800 | 0.2614 | - | - |
| 2.7288 | 1900 | 0.0716 | - | - |
| 2.8723 | 2000 | 0.2655 | 0.2546 | 0.4152 |
| 3.0172 | 2100 | 0.2187 | - | - |
| 3.1607 | 2200 | 0.2623 | - | - |
| 3.3042 | 2300 | 0.2462 | - | - |
| 3.4476 | 2400 | 0.2363 | - | - |
| 3.5911 | 2500 | 0.213 | 0.2866 | 0.4227 |
| 3.7346 | 2600 | 0.0487 | - | - |
| 3.8780 | 2700 | 0.222 | - | - |
| 4.0230 | 2800 | 0.1851 | - | - |
| 4.1664 | 2900 | 0.224 | - | - |
| 4.3099 | 3000 | 0.2111 | 0.2562 | 0.4215 |
| 4.4534 | 3100 | 0.1984 | - | - |
| 4.5968 | 3200 | 0.1707 | - | - |
| 4.7403 | 3300 | 0.0331 | - | - |
| 4.8838 | 3400 | 0.1896 | - | - |
| 5.0287 | 3500 | 0.1548 | 0.2643 | 0.4151 |
| 5.1722 | 3600 | 0.19 | - | - |
| 5.3156 | 3700 | 0.1656 | - | - |
| 5.4591 | 3800 | 0.1626 | - | - |
| 5.6026 | 3900 | 0.1303 | - | - |
| 5.7461 | 4000 | 0.0264 | 0.2952 | 0.4186 |
| 5.8895 | 4100 | 0.1563 | - | - |
| 6.0344 | 4200 | 0.1286 | - | - |
| 6.1779 | 4300 | 0.1436 | - | - |
| 6.3214 | 4400 | 0.1352 | - | - |
| 6.4648 | 4500 | 0.1344 | 0.2668 | 0.4218 |
| 6.6083 | 4600 | 0.1069 | - | - |
| 6.7518 | 4700 | 0.0171 | - | - |
| 6.8953 | 4800 | 0.1246 | - | - |
| 7.0402 | 4900 | 0.1074 | - | - |
| 7.1836 | 5000 | 0.1192 | 0.2837 | 0.4166 |
| 7.3271 | 5100 | 0.1176 | - | - |
| 7.4706 | 5200 | 0.111 | - | - |
| 7.6141 | 5300 | 0.0889 | - | - |
| 7.7575 | 5400 | 0.0202 | - | - |
| 7.9010 | 5500 | 0.1059 | 0.2797 | 0.4166 |
| 8.0459 | 5600 | 0.0854 | - | - |
| 8.1894 | 5700 | 0.0989 | - | - |
| 8.3329 | 5800 | 0.0963 | - | - |
| 8.4763 | 5900 | 0.0967 | - | - |
| 8.6198 | 6000 | 0.0635 | 0.2974 | 0.4223 |
| 8.7633 | 6100 | 0.0215 | - | - |
| 8.9067 | 6200 | 0.0897 | - | - |
| 9.0516 | 6300 | 0.0693 | - | - |
| 9.1951 | 6400 | 0.0913 | - | - |
| 9.3386 | 6500 | 0.0883 | 0.2812 | 0.4171 |
| 9.4821 | 6600 | 0.0849 | - | - |
| 9.6255 | 6700 | 0.0525 | - | - |
| 9.7690 | 6800 | 0.0196 | - | - |
| 9.9125 | 6900 | 0.0799 | - | - |
| 10.0574 | 7000 | 0.0603 | 0.2899 | 0.4132 |
| 10.2009 | 7100 | 0.0816 | - | - |
| 10.3443 | 7200 | 0.0771 | - | - |
| 10.4878 | 7300 | 0.0746 | - | - |
| 10.6313 | 7400 | 0.0373 | - | - |
| **10.7747** | **7500** | **0.0181** | **0.3148** | **0.419** |
| 10.9182 | 7600 | 0.0702 | - | - |
| 11.0631 | 7700 | 0.0531 | - | - |
| 11.2066 | 7800 | 0.0671 | - | - |
| 11.3501 | 7900 | 0.0742 | - | - |
| 11.4935 | 8000 | 0.0728 | 0.2878 | 0.4177 |
| 11.6370 | 8100 | 0.0331 | - | - |
| 11.7805 | 8200 | 0.0206 | - | - |
| 11.9240 | 8300 | 0.0605 | - | - |
| 12.0689 | 8400 | 0.05 | - | - |
| 12.2123 | 8500 | 0.06 | 0.3169 | 0.4180 |
| 12.3558 | 8600 | 0.0613 | - | - |
| 12.4993 | 8700 | 0.0649 | - | - |
| 12.6428 | 8800 | 0.0257 | - | - |
| 12.7862 | 8900 | 0.0184 | - | - |
| 12.9297 | 9000 | 0.055 | 0.3107 | 0.4189 |
| 13.0746 | 9100 | 0.0417 | - | - |
| 13.2181 | 9200 | 0.0537 | - | - |
| 13.3615 | 9300 | 0.0558 | - | - |
| 13.5050 | 9400 | 0.0619 | - | - |
| 13.6485 | 9500 | 0.0217 | 0.3140 | 0.4173 |
| 13.7920 | 9600 | 0.0257 | - | - |
| 13.9354 | 9700 | 0.0398 | - | - |
| 14.0803 | 9800 | 0.041 | - | - |
| 14.2238 | 9900 | 0.0451 | - | - |
| 14.3673 | 10000 | 0.0485 | 0.3085 | 0.4188 |
| 14.5108 | 10100 | 0.0565 | - | - |
| 14.6542 | 10200 | 0.0159 | - | - |
| 14.7977 | 10300 | 0.0258 | - | - |
| 14.9412 | 10400 | 0.0364 | - | - |
| 15.0861 | 10500 | 0.0368 | 0.3144 | 0.4163 |
| 15.2296 | 10600 | 0.0447 | - | - |
| 15.3730 | 10700 | 0.0479 | - | - |
| 15.5165 | 10800 | 0.0535 | - | - |
| 15.6600 | 10900 | 0.0139 | - | - |
| 15.8034 | 11000 | 0.0257 | 0.3149 | 0.4151 |
| 15.9469 | 11100 | 0.0324 | - | - |
| 16.0918 | 11200 | 0.0374 | - | - |
| 16.2353 | 11300 | 0.0339 | - | - |
| 16.3788 | 11400 | 0.0423 | - | - |
| 16.5222 | 11500 | 0.0512 | 0.3209 | 0.4164 |
| 16.6657 | 11600 | 0.0121 | - | - |
| 16.8092 | 11700 | 0.0245 | - | - |
| 16.9527 | 11800 | 0.0323 | - | - |
| 17.0976 | 11900 | 0.0321 | - | - |
| 17.2410 | 12000 | 0.034 | 0.3211 | 0.4140 |
| 17.3845 | 12100 | 0.0387 | - | - |
| 17.5280 | 12200 | 0.0482 | - | - |
| 17.6714 | 12300 | 0.0096 | - | - |
| 17.8149 | 12400 | 0.0252 | - | - |
| 17.9584 | 12500 | 0.0299 | 0.3169 | 0.4170 |
| 18.1033 | 12600 | 0.0351 | - | - |
| 18.2468 | 12700 | 0.032 | - | - |
| 18.3902 | 12800 | 0.0348 | - | - |
| 18.5337 | 12900 | 0.0452 | - | - |
| 18.6772 | 13000 | 0.0076 | 0.3273 | 0.4158 |
| 18.8207 | 13100 | 0.0241 | - | - |
| 18.9641 | 13200 | 0.0277 | - | - |
| 19.1090 | 13300 | 0.0331 | - | - |
| 19.2525 | 13400 | 0.0264 | - | - |
| 19.3960 | 13500 | 0.0311 | 0.3272 | 0.4151 |
| 19.5395 | 13600 | 0.0437 | - | - |
| 19.6829 | 13700 | 0.0049 | - | - |
| 19.8264 | 13800 | 0.0263 | - | - |
| 19.9699 | 13900 | 0.0231 | - | - |
| 20.1148 | 14000 | 0.0303 | 0.3293 | 0.4200 |
| 20.2582 | 14100 | 0.0229 | - | - |
| 20.4017 | 14200 | 0.032 | - | - |
| 20.5452 | 14300 | 0.0395 | - | - |
| 20.6887 | 14400 | 0.0045 | - | - |
| 20.8321 | 14500 | 0.0244 | 0.3202 | 0.4144 |
| 20.9756 | 14600 | 0.0219 | - | - |
| 21.1205 | 14700 | 0.0291 | - | - |
| 21.2640 | 14800 | 0.0212 | - | - |
| 21.4075 | 14900 | 0.029 | - | - |
| 21.5509 | 15000 | 0.0357 | 0.3312 | 0.4147 |
| 21.6944 | 15100 | 0.0025 | - | - |
| 21.8379 | 15200 | 0.0252 | - | - |
| 21.9813 | 15300 | 0.0229 | - | - |
| 22.1263 | 15400 | 0.0261 | - | - |
| 22.2697 | 15500 | 0.0198 | 0.3392 | 0.4123 |
| 22.4132 | 15600 | 0.0259 | - | - |
| 22.5567 | 15700 | 0.0343 | - | - |
| 22.7001 | 15800 | 0.0022 | - | - |
| 22.8436 | 15900 | 0.0237 | - | - |
| 22.9871 | 16000 | 0.0199 | 0.3346 | 0.4146 |
| 23.1320 | 16100 | 0.0263 | - | - |
| 23.2755 | 16200 | 0.0173 | - | - |
| 23.4189 | 16300 | 0.0276 | - | - |
| 23.5624 | 16400 | 0.03 | - | - |
| 23.7059 | 16500 | 0.0022 | 0.3430 | 0.4195 |
| 23.8494 | 16600 | 0.0253 | - | - |
| 23.9928 | 16700 | 0.0182 | - | - |
| 24.1377 | 16800 | 0.0216 | - | - |
| 24.2812 | 16900 | 0.0194 | - | - |
| 24.4247 | 17000 | 0.0242 | 0.3335 | 0.4132 |
| 24.5681 | 17100 | 0.0289 | - | - |
| 24.7116 | 17200 | 0.0013 | - | - |
| 24.8551 | 17300 | 0.0253 | - | - |
| 24.9986 | 17400 | 0.0137 | - | - |
| 25.1435 | 17500 | 0.0219 | 0.3481 | 0.4118 |
| 25.2869 | 17600 | 0.017 | - | - |
| 25.4304 | 17700 | 0.0261 | - | - |
| 25.5739 | 17800 | 0.0298 | - | - |
| 25.7174 | 17900 | 0.0013 | - | - |
| 25.8608 | 18000 | 0.0257 | 0.3407 | 0.4160 |
| 26.0057 | 18100 | 0.014 | - | - |
| 26.1492 | 18200 | 0.0215 | - | - |
| 26.2927 | 18300 | 0.0161 | - | - |
| 26.4362 | 18400 | 0.0228 | - | - |
| 26.5796 | 18500 | 0.0246 | 0.3404 | 0.4131 |
| 26.7231 | 18600 | 0.0017 | - | - |
| 26.8666 | 18700 | 0.0244 | - | - |
| 27.0115 | 18800 | 0.0124 | - | - |
| 27.1549 | 18900 | 0.019 | - | - |
| 27.2984 | 19000 | 0.0151 | 0.3451 | 0.4139 |
| 27.4419 | 19100 | 0.0216 | - | - |
| 27.5854 | 19200 | 0.0255 | - | - |
| 27.7288 | 19300 | 0.0016 | - | - |
| 27.8723 | 19400 | 0.0251 | - | - |
| 28.0172 | 19500 | 0.0133 | 0.3416 | 0.4109 |
| 28.1607 | 19600 | 0.016 | - | - |
| 28.3042 | 19700 | 0.0186 | - | - |
| 28.4476 | 19800 | 0.0185 | - | - |
| 28.5911 | 19900 | 0.0225 | - | - |
| 28.7346 | 20000 | 0.0009 | 0.3463 | 0.4144 |
| 28.8780 | 20100 | 0.0249 | - | - |
| 29.0230 | 20200 | 0.0132 | - | - |
| 29.1664 | 20300 | 0.0145 | - | - |
| 29.3099 | 20400 | 0.0174 | - | - |
| 29.4534 | 20500 | 0.0172 | 0.3425 | 0.4092 |
| 29.5968 | 20600 | 0.0235 | - | - |
| 29.7403 | 20700 | 0.0009 | - | - |
| 29.8838 | 20800 | 0.0242 | - | - |
| 30.0287 | 20900 | 0.0128 | - | - |
| 30.1722 | 21000 | 0.0133 | 0.3482 | 0.4131 |
| 30.3156 | 21100 | 0.0158 | - | - |
| 30.4591 | 21200 | 0.0226 | - | - |
| 30.6026 | 21300 | 0.0188 | - | - |
| 30.7461 | 21400 | 0.0009 | - | - |
| 30.8895 | 21500 | 0.0249 | 0.3483 | 0.4132 |
| 31.0344 | 21600 | 0.0116 | - | - |
| 31.1779 | 21700 | 0.0117 | - | - |
| 31.3214 | 21800 | 0.0162 | - | - |
| 31.4648 | 21900 | 0.0184 | - | - |
| 31.6083 | 22000 | 0.0178 | 0.3390 | 0.4145 |
| 31.7518 | 22100 | 0.0012 | - | - |
| 31.8953 | 22200 | 0.0215 | - | - |
| 32.0402 | 22300 | 0.014 | - | - |
| 32.1836 | 22400 | 0.0105 | - | - |
| 32.3271 | 22500 | 0.0131 | 0.3556 | 0.4144 |
| 32.4706 | 22600 | 0.0199 | - | - |
| 32.6141 | 22700 | 0.0158 | - | - |
| 32.7575 | 22800 | 0.0018 | - | - |
| 32.9010 | 22900 | 0.0236 | - | - |
| 33.0459 | 23000 | 0.0131 | 0.3480 | 0.4136 |
| 33.1894 | 23100 | 0.0121 | - | - |
| 33.3329 | 23200 | 0.0164 | - | - |
| 33.4763 | 23300 | 0.0209 | - | - |
| 33.6198 | 23400 | 0.0119 | - | - |
| 33.7633 | 23500 | 0.0029 | 0.3575 | 0.4180 |
| 33.9067 | 23600 | 0.0201 | - | - |
| 34.0516 | 23700 | 0.0121 | - | - |
| 34.1951 | 23800 | 0.0109 | - | - |
| 34.3386 | 23900 | 0.0132 | - | - |
| 34.4821 | 24000 | 0.0203 | 0.3446 | 0.4141 |
| 34.6255 | 24100 | 0.0087 | - | - |
| 34.7690 | 24200 | 0.0032 | - | - |
| 34.9125 | 24300 | 0.0182 | - | - |
| 35.0574 | 24400 | 0.0116 | - | - |
| 35.2009 | 24500 | 0.0105 | 0.3587 | 0.4117 |
| 35.3443 | 24600 | 0.018 | - | - |
| 35.4878 | 24700 | 0.0194 | - | - |
| 35.6313 | 24800 | 0.0076 | - | - |
| 35.7747 | 24900 | 0.0029 | - | - |
| 35.9182 | 25000 | 0.0167 | 0.3529 | 0.4156 |
| 36.0631 | 25100 | 0.0105 | - | - |
| 36.2066 | 25200 | 0.0097 | - | - |
| 36.3501 | 25300 | 0.0165 | - | - |
| 36.4935 | 25400 | 0.0187 | - | - |
| 36.6370 | 25500 | 0.0062 | 0.3517 | 0.4173 |
| 36.7805 | 25600 | 0.0034 | - | - |
| 36.9240 | 25700 | 0.0173 | - | - |
| 37.0689 | 25800 | 0.0091 | - | - |
| 37.2123 | 25900 | 0.0093 | - | - |
| 37.3558 | 26000 | 0.0152 | 0.3605 | 0.4147 |
| 37.4993 | 26100 | 0.0193 | - | - |
| 37.6428 | 26200 | 0.0065 | - | - |
| 37.7862 | 26300 | 0.0036 | - | - |
| 37.9297 | 26400 | 0.017 | - | - |
| 38.0746 | 26500 | 0.009 | 0.3627 | 0.4178 |
| 38.2181 | 26600 | 0.0087 | - | - |
| 38.3615 | 26700 | 0.0129 | - | - |
| 38.5050 | 26800 | 0.0199 | - | - |
| 38.6485 | 26900 | 0.0047 | - | - |
| 38.7920 | 27000 | 0.0104 | 0.3535 | 0.4191 |
| 38.9354 | 27100 | 0.0106 | - | - |
| 39.0803 | 27200 | 0.0083 | - | - |
| 39.2238 | 27300 | 0.0091 | - | - |
| 39.3673 | 27400 | 0.0143 | - | - |
| 39.5108 | 27500 | 0.018 | 0.3586 | 0.4137 |
| 39.6542 | 27600 | 0.0055 | - | - |
| 39.7977 | 27700 | 0.0097 | - | - |
| 39.9412 | 27800 | 0.0111 | - | - |
| 40.0861 | 27900 | 0.0091 | - | - |
| 40.2296 | 28000 | 0.009 | 0.3540 | 0.4166 |
| 40.3730 | 28100 | 0.0145 | - | - |
| 40.5165 | 28200 | 0.0165 | - | - |
| 40.6600 | 28300 | 0.0041 | - | - |
| 40.8034 | 28400 | 0.009 | - | - |
| 40.9469 | 28500 | 0.0091 | 0.3541 | 0.4159 |
| 41.0918 | 28600 | 0.0106 | - | - |
| 41.2353 | 28700 | 0.0064 | - | - |
| 41.3788 | 28800 | 0.0125 | - | - |
| 41.5222 | 28900 | 0.0172 | - | - |
| 41.6657 | 29000 | 0.0028 | 0.3550 | 0.4151 |
| 41.8092 | 29100 | 0.0097 | - | - |
| 41.9527 | 29200 | 0.0086 | - | - |
| 42.0976 | 29300 | 0.0099 | - | - |
| 42.2410 | 29400 | 0.0064 | - | - |
| 42.3845 | 29500 | 0.0127 | 0.3619 | 0.4150 |
| 42.5280 | 29600 | 0.0157 | - | - |
| 42.6714 | 29700 | 0.0025 | - | - |
| 42.8149 | 29800 | 0.0095 | - | - |
| 42.9584 | 29900 | 0.0087 | - | - |
| 43.1033 | 30000 | 0.0094 | 0.3591 | 0.4153 |
| 43.2468 | 30100 | 0.007 | - | - |
| 43.3902 | 30200 | 0.0114 | - | - |
| 43.5337 | 30300 | 0.0166 | - | - |
| 43.6772 | 30400 | 0.0023 | - | - |
| 43.8207 | 30500 | 0.01 | 0.3582 | 0.4172 |
| 43.9641 | 30600 | 0.0097 | - | - |
| 44.1090 | 30700 | 0.01 | - | - |
| 44.2525 | 30800 | 0.007 | - | - |
| 44.3960 | 30900 | 0.0106 | - | - |
| 44.5395 | 31000 | 0.0164 | 0.3626 | 0.4151 |
| 44.6829 | 31100 | 0.0017 | - | - |
| 44.8264 | 31200 | 0.0113 | - | - |
| 44.9699 | 31300 | 0.0081 | - | - |
| 45.1148 | 31400 | 0.0095 | - | - |
| 45.2582 | 31500 | 0.0061 | 0.3669 | 0.4152 |
| 45.4017 | 31600 | 0.0111 | - | - |
| 45.5452 | 31700 | 0.0157 | - | - |
| 45.6887 | 31800 | 0.0015 | - | - |
| 45.8321 | 31900 | 0.0109 | - | - |
| 45.9756 | 32000 | 0.0085 | 0.3595 | 0.4139 |
| 46.1205 | 32100 | 0.0096 | - | - |
| 46.2640 | 32200 | 0.0062 | - | - |
| 46.4075 | 32300 | 0.0111 | - | - |
| 46.5509 | 32400 | 0.017 | - | - |
| 46.6944 | 32500 | 0.0013 | 0.3631 | 0.4154 |
| 46.8379 | 32600 | 0.0123 | - | - |
| 46.9813 | 32700 | 0.0076 | - | - |
| 47.1263 | 32800 | 0.0088 | - | - |
| 47.2697 | 32900 | 0.0065 | - | - |
| 47.4132 | 33000 | 0.0116 | 0.3656 | 0.4148 |
| 47.5567 | 33100 | 0.0142 | - | - |
| 47.7001 | 33200 | 0.0009 | - | - |
| 47.8436 | 33300 | 0.0101 | - | - |
| 47.9871 | 33400 | 0.0069 | - | - |
| 48.1320 | 33500 | 0.0087 | 0.3643 | 0.4160 |
| 48.2755 | 33600 | 0.005 | - | - |
| 48.4189 | 33700 | 0.0118 | - | - |
| 48.5624 | 33800 | 0.0147 | - | - |
| 48.7059 | 33900 | 0.0008 | - | - |
| 48.8494 | 34000 | 0.0115 | 0.3632 | 0.4158 |
| 48.9928 | 34100 | 0.006 | - | - |
| 49.1377 | 34200 | 0.0089 | - | - |
| 49.2812 | 34300 | 0.0063 | - | - |
| 49.4247 | 34400 | 0.0126 | - | - |
| 49.5681 | 34500 | 0.0142 | 0.3643 | 0.4157 |
| 49.7116 | 34600 | 0.0008 | - | - |
| 49.8551 | 34700 | 0.0137 | - | - |
| 49.9986 | 34800 | 0.0044 | 0.3148 | 0.4190 |
* 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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