metadata
base_model: TaylorAI/bge-micro-v2
library_name: sentence-transformers
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1814
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
Based on the provided information, it seems like you are listing various
substances and the potential side effects associated with them. Here's a
summary:
**Substances and Related Side Effects:**
1. **Amitriptyline**
- Hyperkeratosis
- Muscle weakness
- Abnormal macular morphology
- Visual impairment
- Anxiety
- Abnormality of the endocrine system
- Hypothyroidism
- Inflammatory abnormality of the skin
- Eczema
- Skin ulcer
- Erythema
- Jaundice
- Hyperhidrosis
- Blurred vision
- Abnormality of extrapyramidal motor function
- Hepatic steatosis
- Increased body weight
- Arrhythmia
- Supraventricular arrhythmia
- Congestive heart failure
- Abnormality of blood and blood-forming tissues
- Thrombocytopenia
- Renal insufficiency
- Fever
- Hypoglycemia
- Dehydration
- Pain
- Esophageal stenosis
- Gait disturbance
sentences:
- >-
Can you provide me with a list of medications that could cause loss of
appetite and a smooth tongue sensation as side effects?
- >-
What are the secondary diseases related to breast cancer characterized
by gene expression changes associated with genomic variations affecting
cell growth and division, and present symptoms like pain, fatigue,
breathing problems, vomiting, changes in bowel movements, and wasting,
particularly during treatment?
- >-
Which drugs targeting the dopamine transporter encoded by SLC6A3 gene
are approved for managing Major Depressive Disorder, Generalized Anxiety
Disorder, neuropathic pain, osteoarthritis, and stress urinary
incontinence?
- source_sentence: >-
Alvespimycin, a derivative of Geldanamycin and a Heat Shock Protein 90
(HSP90) inhibitor, falls under the drug category on DrugBank. It
encompasses Amides, HSP90 Heat-Shock Proteins, Lactams, and Quinones. This
compound is currently under investigation for its antineoplastic potential
in treating solid tumors, advanced solid tumors, or acute myeloid
leukemia. Alvespimycin's typical half-life spans from 9.9 to 54.1 hours,
with a median duration of 18.2 hours, making it a longer-acting drug in
its pharmacodynamics profile. Alvespimycin functions by inhibiting HSP90,
which consequently disrupts the correct folding and function of
oncoproteins derived from HSP90 client proteins, a critical role in
cellular proliferation, and apoptosis suppression. The medication targets
oncogenic kinases like BRAF, inducing their proteasomal degradation and
facilitating depletion of oncoproteins. Notably, Alvespimycin shows a
minimal degree of protein binding and is more selective in its effect on
tumors compared to normal tissues. The drug also aids in increasing the
potency of telomerase inhibition by imetelstat, as demonstrated in
pre-clinical models of human osteosarcoma.
sentences:
- >-
Could you give me a list of medications that interact with the HSP90AA1
gene or protein and have a metabolic half-life ranging from 9.9 to 54.1
hours?
- >-
Which diseases are categorized as forerunners or variations of benign
cervical tumors in current medical classifications?
- >-
Can you give me an overview of diseases related to SLC13A5 gene
abnormalities that involve dysregulation of enzyme activity?
- source_sentence: >-
The gene DRAXIN, also known by various aliases such as 'AGPA3119',
'C1orf187', 'UNQ3119', and 'neucrin', is located on chromosome 1 in the
genomic region defined by its start position at 11691710 and end position
at 11725857. DRAXIN, classified as "dorsal inhibitory axon guidance
protein", is predicted to play a role in the inhibition of the canonical
Wnt signaling pathway, negative regulation of neuron projection
development, and nervous system development. It is also indicated to be
active in the extracellular region.
Studies have revealed that this protein interacts with another gene (NTN1)
and is associated with a range of diseases including Parkinson disease,
juvenile onset Parkinson disease 19A, early-onset
parkinsonism-intellectual disability syndrome, parkinsonian-pyramidal
syndrome, X-linked parkinsonism-spasticity syndrome,
hemiparkinsonism-hemiatrophy syndrome, atypical juvenile parkinsonism, and
hereditary late onset Parkinson disease. It is involved across various
biological processes such as Wnt signaling pathway, negative regulation of
the canonical Wnt pathway, negative regulation of axon extension, and
negative regulation of neuron apoptotic process. DRAXIN is known to have
expression in numerous anatomical entities like blood, prefrontal cortex,
female reproductive system, brain, cerebral cortex, uterus, endometrium,
frontal cortex, temporal lobe, amygdala, forebrain, neocortex, Ammon's
horn, cerebellum, cerebellar cortex, and dorsolateral prefrontal cortex;
however, its expression is absent in colonic mucosa, quadriceps femoris,
vastus lateralis, deltoid, biceps
sentences:
- >-
What are the common Alzheimer's treatments that could cause chest
discomfort, and can you list those with a duration of effect lasting
about three days?
- >-
Which genes or proteins are not expressed in either the small intestinal
or colonic mucosal tissues?
- >-
Identify the Y-linked gene associated with spermatogenic failure that's
located in the Y chromosome's nonrecombining zone and exclusively
expressed in testes.
- source_sentence: >-
TRMT5, also known by aliases such as COXPD26, KIAA1393, PNSED, and TRM5,
is a gene encoding the tRNA methyltransferase 5. This enzyme is
responsible for methylating the N1 position of guanosine-37 (G37) in
specific tRNAs using S-adenosyl methionine. It plays a role in modifying
tRNAs, which contain 13 to 14 nucleotides modified posttranscriptionally
by nucleotide-specific enzymes (Brule
sentences:
- >-
Which gene or protein is known to interact with the one associated with
defective ABCB11, which leads to PFIC2 and BRIC2, and plays a regulatory
role in the expression of genes critical for liver development and
functionality?
- >-
Which genes or proteins are known to interact with tRNA
(guanine(37)-N(1))-methyltransferase activity?
- >-
What is the biosynthetic pathway that falls under 'Creation of C4 and C2
activators' and also involves the MASP1 gene or its protein product?
- source_sentence: >-
Muscular dystrophy is a group of inherited disorders characterized by
progressive muscle weakness and wasting. Here's a concise overview of the
information you've provided:
### Types of Muscular Dystrophy:
- **Duchenne Muscular Dystrophy**: Most common in young boys,
characterized by severe muscle weakness and consequent inability to walk
by adolescence.
- **Becker Muscular Dystrophy**: Less severe than Duchenne but still
progressive, affecting males.
- **Facioscapulohumeral Muscular Dystrophy (FSHD)**: Affects the face,
shoulder, and upper arm muscles, common in the teenage to adult years.
- **
sentences:
- >-
Identify a metabolic pathway that is associated with both glyoxylate
metabolism and glycine degradation and is capable of interacting with a
common gene or protein.
- >-
Which gene/protein belonging to the activator 1 small subunit family is
involved in interactions with the gene/protein associated with
compromised DNA recombination inhibition at telomeres resulting from
DAXX mutations?
- >-
I need details on a disease linked to the COL6A2 gene, presenting with
progressive muscle weakening in specific groups and worsening muscle
strength over time.
model-index:
- name: SentenceTransformer based on TaylorAI/bge-micro-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.41089108910891087
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.49504950495049505
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5346534653465347
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5693069306930693
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.41089108910891087
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.165016501650165
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10693069306930691
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05693069306930693
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.41089108910891087
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.49504950495049505
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5346534653465347
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5693069306930693
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48660626760149667
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.46036657237152295
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4675921280486482
name: Cosine Map@100
SentenceTransformer based on TaylorAI/bge-micro-v2
This is a sentence-transformers model finetuned from TaylorAI/bge-micro-v2 on the json dataset. It maps sentences & paragraphs to a 384-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: TaylorAI/bge-micro-v2
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("FareedKhan/TaylorAI_bge-micro-v2_FareedKhan_prime_synthetic_data_2k_10_32")
# Run inference
sentences = [
"\n\nMuscular dystrophy is a group of inherited disorders characterized by progressive muscle weakness and wasting. Here's a concise overview of the information you've provided:\n\n### Types of Muscular Dystrophy:\n- **Duchenne Muscular Dystrophy**: Most common in young boys, characterized by severe muscle weakness and consequent inability to walk by adolescence.\n- **Becker Muscular Dystrophy**: Less severe than Duchenne but still progressive, affecting males.\n- **Facioscapulohumeral Muscular Dystrophy (FSHD)**: Affects the face, shoulder, and upper arm muscles, common in the teenage to adult years.\n- **",
'I need details on a disease linked to the COL6A2 gene, presenting with progressive muscle weakening in specific groups and worsening muscle strength over time.',
'Identify a metabolic pathway that is associated with both glyoxylate metabolism and glycine degradation and is capable of interacting with a common gene or protein.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
dim_384
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.4109 |
cosine_accuracy@3 | 0.495 |
cosine_accuracy@5 | 0.5347 |
cosine_accuracy@10 | 0.5693 |
cosine_precision@1 | 0.4109 |
cosine_precision@3 | 0.165 |
cosine_precision@5 | 0.1069 |
cosine_precision@10 | 0.0569 |
cosine_recall@1 | 0.4109 |
cosine_recall@3 | 0.495 |
cosine_recall@5 | 0.5347 |
cosine_recall@10 | 0.5693 |
cosine_ndcg@10 | 0.4866 |
cosine_mrr@10 | 0.4604 |
cosine_map@100 | 0.4676 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 1,814 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 2 tokens
- mean: 247.16 tokens
- max: 512 tokens
- min: 13 tokens
- mean: 35.28 tokens
- max: 113 tokens
- Samples:
positive anchor
Hemophilia is an inherited bleeding disorder that occurs when a person's body does not produce enough of certain clotting factors, leading to prolonged bleeding and, in severe cases, spontaneous bleeding into joints and muscles. The disorder is typically associated with mutations in the genes that code for clotting factors VIII (for hemophilia A) and IX (for hemophilia B). It can be categorized based on the specific clotting factor affected and the mode of inheritance.
### Risk Factors
The biggest risk factor for hemophilia is a family history of the disorder. If a family member, particularly a parent or a close relative, has hemophilia, there is an increased risk for the disease due to the genetic predisposition.
### Genetic Inheritance
- Hemophilia A (Severe) or Factor VIII deficiency: Often affects males due to the inheritance pattern X-linked recessive. A carrier female has a 50% chance of passing the gene to each of her offspring.
- Hemophilia B (Severe) or Factor IX deficiency: Also typically X-linked recessive, mostly affecting males. Carrier females are likely to pass the gene to their male offspring only.
### Complications and Symptoms
- Abnormal bleeding: This is the most common symptom, ranging from mild to life-threatening.
- Subcutaneous hemorrhage and intracranial hemorrhage: These can lead to serious complications and require immediate medical attention.
- Joint damage: Frequent bleeding into joints can result in arthritis, joint destruction, and limitation of joint mobility.
- Gastrointestinal, genitourinary, and epistaxis: These are other sites where bleeding can occur, often with minor trauma.
### Treatment and Management
Treatment for hemophilia often involves replacing the missing clotting factors using infused or transfused factors. This can be through Factor VIII concentrate for hemophilia A or Factor IX concentrate for hemophilia B. Prophylactic treatments are often administered to prevent bleeding episodes and maintain normal joint function.
### Diagnosis
Diagnosis of hemophilia is typically made through a series of blood tests to measure clotting times and factor levels. Genetic testing is also recommended in families with a history of hemophilia to identify carriers and those with more severe symptoms.
### See a Doctor
It's important to see a doctor if you or your child shows signs of prolonged bleeding or if there is a family history of hemophilia. Early diagnosis and appropriate treatment can significantly improve outcomes and quality of life.
### Carrying and Symptoms in Female Carriers
While female carriers are usually asymptomatic, they can experience mild symptoms under specific circumstances such as during pregnancy (gastrointestinal bleeding) or menopause (menorrhagia). Genetic testing can confirm an asymptomatic carrier status, which is important for family planning and counseling.
### In Conclusion
Hemophilia is a complex condition that requires careful management to prevent complications and maintain quality of life. Early diagnosis, genetic counseling, and proper treatment are crucial for managing this inherited bleeding disorder effectively.Which condition should be avoided when prescribing medications for outdated forms of contact dermatitis resulting from poison oak exposure?
Assistant: Diabetes insipidus, a rare but serious condition, can manifest with a series of symptoms and has diverse impacts on various systems of the body. Primarily characterized by increased thirst, significant urination, and dehydration, diabetes insipidus requires prompt medical intervention.
Symptoms and Impacts:
1. Polydipsia (increased thirst) and polyuria (frequent urination) are the primary symptoms, typically exceeding 10 liters of fluid intake and urine output per day.
2. Dehydration can result from excessive fluid loss unless compensated, causing electrolyteWhat medical condition could I have that involves persistent thirst, frequent urination, and unexplained weight loss, and is associated with a familial disorder affecting water balance similar to diabetes insipidus, but not identical, as it involves an inability to concentrate urine? My father has it, and my doctor suggested managing salt intake and water consumption, mentioning that medication may be available to reduce the urination. What is the name of this disease?
The pathway described in this document is titled "p75 NTR receptor-mediated signalling" which suggests that it centers around the activity of the p75 neurotrophin receptor (p75 NTR), a cell surface receptor that plays a crucial role in neuronal development, survival, and function.
### Key Components and Their Roles:
- Neurotrophin (NGF or Nerve Growth Factor): This is a ligand that binds to the p75 NTR. Binding of NGF to p75 NTR initiates a cascade of events resulting in various cellular responses.
- p75 NTR: The receptor itself is pivotal, as its binding with ligands like NGF modulates signal transduction in cells, affecting survival, differentiation, and various aspects of cellular metabolism and function.
- Sphingomyelinase (SMPD2): This gene/protein is implicated in the pathway, with involvement in modulating ceramide production upon NGF Binding to p75 NTR. Sphingomyelinase is activated by the NGF:p75NTR complex, suggesting an integral role in the effector phase of the signaling cascade.
- Ceramide: A lipid derived from sphingomyelin that plays a key role in cellular signaling. Ceramide's production upon ligand-receptor binding can lead to either cell survival or apoptosis depending on the context within specific cell types.
- JNK (c-Jun N-terminal kinase): This is a serine/threonine kinase that can be activated by ceramide and is involved in various cellular processes including apoptosis, cell cycle regulation, and differentiation.
### Pathway Description:
The pathway described includes mechanisms by which ligand binding to p75 NTR leads to ceramide production, which inWhich signaling pathway interacts with both p75 NTR receptor signaling and the nerve growth factor (NGF) gene/protein in a hierarchical manner?
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384 ], "matryoshka_weights": [ 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 32learning_rate
: 1e-05num_train_epochs
: 10warmup_ratio
: 0.1bf16
: Truetf32
: Falseload_best_model_at_end
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Falselocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | dim_384_cosine_map@100 |
---|---|---|---|
0 | 0 | - | 0.4238 |
0.1754 | 10 | 1.9916 | - |
0.3509 | 20 | 1.8049 | - |
0.5263 | 30 | 1.8366 | - |
0.7018 | 40 | 1.8585 | - |
0.8772 | 50 | 1.7288 | - |
1.0 | 57 | - | 0.4326 |
1.0526 | 60 | 1.6438 | - |
1.2281 | 70 | 1.5404 | - |
1.4035 | 80 | 1.6168 | - |
1.5789 | 90 | 1.5432 | - |
1.7544 | 100 | 1.4976 | - |
1.9298 | 110 | 1.5275 | - |
2.0 | 114 | - | 0.4422 |
2.1053 | 120 | 1.3276 | - |
2.2807 | 130 | 1.3629 | - |
2.4561 | 140 | 1.4108 | - |
2.6316 | 150 | 1.3338 | - |
2.8070 | 160 | 1.4043 | - |
2.9825 | 170 | 1.4664 | - |
3.0 | 171 | - | 0.4487 |
3.1579 | 180 | 1.2225 | - |
3.3333 | 190 | 1.2557 | - |
3.5088 | 200 | 1.3518 | - |
3.6842 | 210 | 1.3227 | - |
3.8596 | 220 | 1.3391 | - |
4.0 | 228 | - | 0.4561 |
4.0351 | 230 | 1.2035 | - |
4.2105 | 240 | 1.197 | - |
4.3860 | 250 | 1.2908 | - |
4.5614 | 260 | 1.1738 | - |
4.7368 | 270 | 1.1855 | - |
4.9123 | 280 | 1.2118 | - |
5.0 | 285 | - | 0.4578 |
5.0877 | 290 | 1.1835 | - |
5.2632 | 300 | 1.1624 | - |
5.4386 | 310 | 1.2075 | - |
5.6140 | 320 | 1.1771 | - |
5.7895 | 330 | 1.0814 | - |
5.9649 | 340 | 1.2039 | - |
6.0 | 342 | - | 0.4584 |
6.1404 | 350 | 1.2029 | - |
6.3158 | 360 | 1.1043 | - |
6.4912 | 370 | 1.2011 | - |
6.6667 | 380 | 1.0401 | - |
6.8421 | 390 | 1.0732 | - |
7.0 | 399 | - | 0.4624 |
7.0175 | 400 | 1.1137 | - |
7.1930 | 410 | 1.0946 | - |
7.3684 | 420 | 1.1581 | - |
7.5439 | 430 | 1.0605 | - |
7.7193 | 440 | 1.076 | - |
7.8947 | 450 | 1.2689 | - |
8.0 | 456 | - | 0.4680 |
8.0702 | 460 | 1.0004 | - |
8.2456 | 470 | 1.1387 | - |
8.4211 | 480 | 1.0652 | - |
8.5965 | 490 | 1.0879 | - |
8.7719 | 500 | 1.1845 | - |
8.9474 | 510 | 1.0979 | - |
9.0 | 513 | - | 0.4684 |
9.1228 | 520 | 1.0588 | - |
9.2982 | 530 | 1.2412 | - |
9.4737 | 540 | 1.0261 | - |
9.6491 | 550 | 1.0919 | - |
9.8246 | 560 | 1.129 | - |
10.0 | 570 | 1.0425 | 0.4676 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.2.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0
Citation
BibTeX
Sentence Transformers
@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",
}
MatryoshkaLoss
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
MultipleNegativesRankingLoss
@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}
}