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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -1,3 +1,868 @@
----
-license: apache-2.0
----
+---
+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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.43.3",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.43.3",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2390c6ed604106f286814c21e7495d06a1dc2ade74a3b334f84080e3cd61de09
+size 437967672

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,72 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 250,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

training_args.bin

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