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metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:9432
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: >-
      Atherosclerosis and coronary heart disease are examples of what type of
      body system disease?
    sentences:
      - >-
        Diseases of the cardiovascular system are common and may be life
        threatening. Examples include atherosclerosis and coronary heart
        disease. A healthy lifestyle can reduce the risk of such diseases
        developing. This includes avoiding smoking, getting regular physical
        activity, and maintaining a healthy percent of body fat.
      - >-
        Osmosis Osmosis is the diffusion of water through a semipermeable
        membrane according to the concentration gradient of water across the
        membrane. Whereas diffusion transports material across membranes and
        within cells, osmosis transports only water across a membrane and the
        membrane limits the diffusion of solutes in the water. Osmosis is a
        special case of diffusion. Water, like other substances, moves from an
        area of higher concentration to one of lower concentration. Imagine a
        beaker with a semipermeable membrane, separating the two sides or halves
        (Figure 3.21). On both sides of the membrane, the water level is the
        same, but there are different concentrations on each side of a dissolved
        substance, or solute, that cannot cross the membrane. If the volume of
        the water is the same, but the concentrations of solute are different,
        then there are also different concentrations of water, the solvent, on
        either side of the membrane.
      - >-
        Circadian rhythms are regular changes in biology or behavior that occur
        in a 24-hour cycle. In humans, for example, blood pressure and body
        temperature change in a regular way throughout each 24-hour day. Animals
        may eat and drink at certain times of day as well. Humans have daily
        cycles of behavior, too. Most people start to get sleepy after dark and
        have a hard time sleeping when it is light outside. In many species,
        including humans, circadian rhythms are controlled by a tiny structure
        called the biological clock . This structure is located in a gland at
        the base of the brain. The biological clock sends signals to the body.
        The signals cause regular changes in behavior and body processes. The
        amount of light entering the eyes helps control the biological clock.
        The clock causes changes that repeat every 24 hours.
  - source_sentence: >-
      How does a cell's membrane keep extracellular materials from mixing with
      it's internal components?
    sentences:
      - >-
        We know that the Universe is expanding. Astronomers have wondered if it
        is expanding fast enough to escape the pull of gravity. Would the
        Universe just expand forever? If it could not escape the pull of
        gravity, would it someday start to contract? This means it would
        eventually get squeezed together in a big crunch. This is the opposite
        of the Big Bang.
      - >-
        Physical properties that do not depend on the amount of substance
        present are called intensive properties . Intensive properties do not
        change with changes of size, shape, or scale. Examples of intensive
        properties are as follows in the Table below .
      - >-
        CHAPTER REVIEW 3.1 The Cell Membrane The cell membrane provides a
        barrier around the cell, separating its internal components from the
        extracellular environment. It is composed of a phospholipid bilayer,
        with hydrophobic internal lipid “tails” and hydrophilic external
        phosphate “heads. ” Various membrane proteins are scattered throughout
        the bilayer, both inserted within it and attached to it peripherally.
        The cell membrane is selectively permeable, allowing only a limited
        number of materials to diffuse through its lipid bilayer. All materials
        that cross the membrane do so using passive (non energy-requiring) or
        active (energy-requiring) transport processes. During passive transport,
        materials move by simple diffusion or by facilitated diffusion through
        the membrane, down their concentration gradient. Water passes through
        the membrane in a diffusion process called osmosis. During active
        transport, energy is expended to assist material movement across the
        membrane in a direction against their concentration gradient. Active
        transport may take place with the help of protein pumps or through the
        use of vesicles.
  - source_sentence: An infection may be intracellular or extracellular, depending on this?
    sentences:
      - >-
        22.3 Magnetic Fields and Magnetic Field Lines • Magnetic fields can be
        pictorially represented by magnetic field lines, the properties of which
        are as follows: 1. The field is tangent to the magnetic field line.
        Field strength is proportional to the line density. Field lines cannot
        cross. Field lines are continuous loops.
      - >-
        Figure 24.13 The lifecycle of an ascomycete is characterized by the
        production of asci during the sexual phase. The haploid phase is the
        predominant phase of the life cycle.
      - >-
        Caffeine is an example of a psychoactive drug. It is found in coffee and
        many other products (see Table below ). Caffeine is a central nervous
        system stimulant . Like other stimulant drugs, it makes you feel more
        awake and alert. Other psychoactive drugs include alcohol, nicotine, and
        marijuana. Each has a different effect on the central nervous system.
        Alcohol, for example, is a depressant . It has the opposite effects of a
        stimulant like caffeine.
  - source_sentence: What does water treatment do to water?
    sentences:
      - >-
        Some solutes, such as sodium acetate, do not recrystallize easily.
        Suppose an exactly saturated solution of sodium acetate is prepared at
        50°C. As it cools back to room temperature, no crystals appear in the
        solution, even though the solubility of sodium acetate is lower at room
        temperature. A supersaturated solution is a solution that contains more
        than the maximum amount of solute that is capable of being dissolved at
        a given temperature. The recrystallization of the excess dissolved
        solute in a supersaturated solution can be initiated by the addition of
        a tiny crystal of solute, called a seed crystal. The seed crystal
        provides a nucleation site on which the excess dissolved crystals can
        begin to grow. Recrystallization from a supersaturated solution is
        typically very fast.
      - >-
        Figure 23.13, the esophagus runs a mainly straight route through the
        mediastinum of the thorax. To enter the abdomen, the esophagus
        penetrates the diaphragm through an opening called the esophageal
        hiatus.
      - >-
        Water treatment is a series of processes that remove unwanted substances
        from water. More processes are needed to purify water for drinking than
        for other uses.
  - source_sentence: >-
      There are only four possible bases that make up each dna nucleotide:
      adenine, guanine, thymine, and?
    sentences:
      - >-
        Metamorphism. This long word means “to change form. “ A rock undergoes
        metamorphism if it is exposed to extreme heat and pressure within the
        crust. With metamorphism , the rock does not melt all the way. The rock
        changes due to heat and pressure. A metamorphic rock may have a new
        mineral composition and/or texture.
      - >-
        Forest and Kim Starr (Flickr:Starr Environmental). Secondary succession
        occurs when nature reclaims areas formerly occupied by life . CC BY 2.0.
      - >-
        The only difference between each nucleotide is the identity of the base.
        There are only four possible bases that make up each DNA nucleotide:
        adenine (A), guanine (G), thymine (T), and cytosine (C).
pipeline_tag: sentence-similarity
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
model-index:
  - name: MNLP M3 Encoder SciQA
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 384
          type: dim_384
        metrics:
          - type: cosine_accuracy@1
            value: 0.6015252621544328
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7959961868446139
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8531935176358436
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9199237368922784
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6015252621544328
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26533206228153794
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17063870352716873
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09199237368922783
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6015252621544328
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7959961868446139
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8531935176358436
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9199237368922784
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.761241503632434
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7104082497314151
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.713601684515785
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.5919923736892279
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7902764537654909
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8360343183984748
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142040038131554
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5919923736892279
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26342548458849696
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16720686367969492
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142040038131555
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5919923736892279
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7902764537654909
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8360343183984748
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142040038131554
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7520267351833514
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.700305279404422
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7038093293311698
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 192
          type: dim_192
        metrics:
          - type: cosine_accuracy@1
            value: 0.5805529075309819
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.782650142993327
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8322211630123928
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9008579599618685
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5805529075309819
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26088338099777564
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16644423260247856
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09008579599618685
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5805529075309819
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.782650142993327
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8322211630123928
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9008579599618685
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7430712975035773
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6923562879234952
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6964841260809953
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.567206863679695
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7607244995233555
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8236415633937083
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.886558627264061
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.567206863679695
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25357483317445184
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16472831267874166
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0886558627264061
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.567206863679695
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7607244995233555
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8236415633937083
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.886558627264061
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7260517487265687
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6746886679679823
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6790430112153837
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 96
          type: dim_96
        metrics:
          - type: cosine_accuracy@1
            value: 0.5471877979027645
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7407054337464252
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8017159199237369
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8722592945662536
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5471877979027645
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2469018112488084
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16034318398474737
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08722592945662536
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5471877979027645
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7407054337464252
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8017159199237369
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8722592945662536
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7097194683573752
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6576811627097615
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6622003643008398
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.5138226882745471
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7016205910390848
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7645376549094376
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8341277407054337
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5138226882745471
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2338735303463616
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1529075309818875
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08341277407054337
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5138226882745471
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7016205910390848
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7645376549094376
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8341277407054337
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6707950308444217
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.618670464690484
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6242158272303533
            name: Cosine Map@100

MNLP M3 Encoder SciQA

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en
License: apache-2.0

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': 256, '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})
  (2): Normalize()
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'There are only four possible bases that make up each dna nucleotide: adenine, guanine, thymine, and?',
    'The only difference between each nucleotide is the identity of the base. There are only four possible bases that make up each DNA nucleotide: adenine (A), guanine (G), thymine (T), and cytosine (C).',
    'Metamorphism. This long word means “to change form. “ A rock undergoes metamorphism if it is exposed to extreme heat and pressure within the crust. With metamorphism , the rock does not melt all the way. The rock changes due to heat and pressure. A metamorphic rock may have a new mineral composition and/or texture.',
]
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

Datasets: dim_384, dim_256, dim_192, dim_128, dim_96 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_384	dim_256	dim_192	dim_128	dim_96	dim_64
cosine_accuracy@1	0.6015	0.592	0.5806	0.5672	0.5472	0.5138
cosine_accuracy@3	0.796	0.7903	0.7827	0.7607	0.7407	0.7016
cosine_accuracy@5	0.8532	0.836	0.8322	0.8236	0.8017	0.7645
cosine_accuracy@10	0.9199	0.9142	0.9009	0.8866	0.8723	0.8341
cosine_precision@1	0.6015	0.592	0.5806	0.5672	0.5472	0.5138
cosine_precision@3	0.2653	0.2634	0.2609	0.2536	0.2469	0.2339
cosine_precision@5	0.1706	0.1672	0.1664	0.1647	0.1603	0.1529
cosine_precision@10	0.092	0.0914	0.0901	0.0887	0.0872	0.0834
cosine_recall@1	0.6015	0.592	0.5806	0.5672	0.5472	0.5138
cosine_recall@3	0.796	0.7903	0.7827	0.7607	0.7407	0.7016
cosine_recall@5	0.8532	0.836	0.8322	0.8236	0.8017	0.7645
cosine_recall@10	0.9199	0.9142	0.9009	0.8866	0.8723	0.8341
cosine_ndcg@10	0.7612	0.752	0.7431	0.7261	0.7097	0.6708
cosine_mrr@10	0.7104	0.7003	0.6924	0.6747	0.6577	0.6187
cosine_map@100	0.7136	0.7038	0.6965	0.679	0.6622	0.6242

Training Details

Training Dataset

json

Dataset: json
Size: 9,432 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 18.15 tokens
max: 60 tokens

min: 10 tokens
mean: 94.56 tokens
max: 256 tokens

	anchor	positive
type	string	string
details	min: 7 tokens mean: 18.15 tokens max: 60 tokens	min: 10 tokens mean: 94.56 tokens max: 256 tokens

Samples:

anchor	positive
`What is the term for atherosclerosis of arteries that supply the heart muscle?`	`Atherosclerosis of arteries that supply the heart muscle is called coronary heart disease . This disease may or may not have symptoms, such as chest pain. As the disease progresses, there is an increased risk of heart attack. A heart attack occurs when the blood supply to part of the heart muscle is blocked and cardiac muscle fibers die. Coronary heart disease is the leading cause of death of adults in the United States.`
`What term describes a drug that has an effect on the central nervous system?`	`Caffeine is an example of a psychoactive drug. It is found in coffee and many other products (see Table below ). Caffeine is a central nervous system stimulant . Like other stimulant drugs, it makes you feel more awake and alert. Other psychoactive drugs include alcohol, nicotine, and marijuana. Each has a different effect on the central nervous system. Alcohol, for example, is a depressant . It has the opposite effects of a stimulant like caffeine.`
`What scale is used to succinctly communicate the acidity or basicity of a solution?`	`The pH scale is used to succinctly communicate the acidity or basicity of a solution.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        384,
        256,
        192,
        128,
        96,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
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: 4
max_steps: -1
lr_scheduler_type: cosine
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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
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}
tp_size: 0
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_fused
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_384_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_192_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_96_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.5424	10	22.4049	-	-	-	-	-	-
1.0	19	-	0.7424	0.7315	0.7263	0.7093	0.6919	0.6575
1.0542	20	16.6616	-	-	-	-	-	-
1.5966	30	16.8367	-	-	-	-	-	-
2.0	38	-	0.7612	0.7520	0.7431	0.7261	0.7097	0.6708

Framework Versions

Python: 3.12.8
Sentence Transformers: 3.4.1
Transformers: 4.51.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.6.0
Tokenizers: 0.21.0

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}
}