checkpoint-72/README.md

---
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: 0.6120114394661582
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8017159199237369
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8541468064823642
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9275500476644424
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6120114394661582
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.267238639974579
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17082936129647283
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09275500476644424
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6120114394661582
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8017159199237369
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8541468064823642
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9275500476644424
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7690377395004954
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7184669450875366
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7210073638258574
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.5977121067683508
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7912297426120114
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8398474737845567
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9151572926596759
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5977121067683508
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26374324753733713
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16796949475691134
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09151572926596759
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5977121067683508
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7912297426120114
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8398474737845567
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9151572926596759
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7558547240171754
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7049529408204341
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7084736712852033
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 192
      type: dim_192
    metrics:
    - type: cosine_accuracy@1
      value: 0.5891325071496664
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.778836987607245
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8331744518589133
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.90371782650143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5891325071496664
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.259612329202415
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16663489037178267
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.090371782650143
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5891325071496664
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.778836987607245
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8331744518589133
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.90371782650143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7467179313530818
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6964694266648511
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7004357679049269
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.5662535748331744
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7626310772163966
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8265014299332698
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8913250714966635
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5662535748331744
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25421035907213213
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16530028598665394
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08913250714966635
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5662535748331744
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7626310772163966
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8265014299332698
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8913250714966635
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7275517192718437
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6752375656331816
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6793502491099088
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 96
      type: dim_96
    metrics:
    - type: cosine_accuracy@1
      value: 0.551954242135367
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7416587225929456
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8093422306959008
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8732125834127741
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.551954242135367
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.24721957419764853
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1618684461391802
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08732125834127741
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.551954242135367
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7416587225929456
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8093422306959008
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8732125834127741
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7119774118711802
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.660333348464903
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6648689218069684
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.5166825548141086
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7044804575786463
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7683508102955195
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8369876072449952
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5166825548141086
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2348268191928821
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1536701620591039
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08369876072449953
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5166825548141086
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7044804575786463
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7683508102955195
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8369876072449952
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6755211859192654
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6239059875618503
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6292715088820261
      name: Cosine Map@100
---

# MNLP M3 Encoder SciQA

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **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](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': 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:

```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 = [
    '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]
```

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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_384`, `dim_256`, `dim_192`, `dim_128`, `dim_96` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_384   | dim_256    | dim_192    | dim_128    | dim_96    | dim_64     |
|:--------------------|:----------|:-----------|:-----------|:-----------|:----------|:-----------|
| cosine_accuracy@1   | 0.612     | 0.5977     | 0.5891     | 0.5663     | 0.552     | 0.5167     |
| cosine_accuracy@3   | 0.8017    | 0.7912     | 0.7788     | 0.7626     | 0.7417    | 0.7045     |
| cosine_accuracy@5   | 0.8541    | 0.8398     | 0.8332     | 0.8265     | 0.8093    | 0.7684     |
| cosine_accuracy@10  | 0.9276    | 0.9152     | 0.9037     | 0.8913     | 0.8732    | 0.837      |
| cosine_precision@1  | 0.612     | 0.5977     | 0.5891     | 0.5663     | 0.552     | 0.5167     |
| cosine_precision@3  | 0.2672    | 0.2637     | 0.2596     | 0.2542     | 0.2472    | 0.2348     |
| cosine_precision@5  | 0.1708    | 0.168      | 0.1666     | 0.1653     | 0.1619    | 0.1537     |
| cosine_precision@10 | 0.0928    | 0.0915     | 0.0904     | 0.0891     | 0.0873    | 0.0837     |
| cosine_recall@1     | 0.612     | 0.5977     | 0.5891     | 0.5663     | 0.552     | 0.5167     |
| cosine_recall@3     | 0.8017    | 0.7912     | 0.7788     | 0.7626     | 0.7417    | 0.7045     |
| cosine_recall@5     | 0.8541    | 0.8398     | 0.8332     | 0.8265     | 0.8093    | 0.7684     |
| cosine_recall@10    | 0.9276    | 0.9152     | 0.9037     | 0.8913     | 0.8732    | 0.837      |
| **cosine_ndcg@10**  | **0.769** | **0.7559** | **0.7467** | **0.7276** | **0.712** | **0.6755** |
| cosine_mrr@10       | 0.7185    | 0.705      | 0.6965     | 0.6752     | 0.6603    | 0.6239     |
| cosine_map@100      | 0.721     | 0.7085     | 0.7004     | 0.6794     | 0.6649    | 0.6293     |

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### Recommendations

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 9,432 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 7 tokens</li><li>mean: 18.15 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 94.56 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
  | anchor                                                                                           | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
  |:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the term for atherosclerosis of arteries that supply the heart muscle?</code>      | <code>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.</code>                              |
  | <code>What term describes a drug that has an effect on the central nervous system?</code>        | <code>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.</code> |
  | <code>What scale is used to succinctly communicate the acidity or basicity of a solution?</code> | <code>The pH scale is used to succinctly communicate the acidity or basicity of a solution.</code>                                                                                                                                                                                                                                                                                                                                                                                 |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "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
<details><summary>Click to expand</summary>

- `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

</details>

### 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                |
| 2.1085 | 40   | 12.8169       | -                      | -                      | -                      | -                      | -                     | -                     |
| 2.6508 | 50   | 13.7826       | -                      | -                      | -                      | -                      | -                     | -                     |
| 3.0    | 57   | -             | 0.7675                 | 0.7548                 | 0.7477                 | 0.7274                 | 0.7125                | 0.6756                |
| 3.1627 | 60   | 12.4455       | -                      | -                      | -                      | -                      | -                     | -                     |
| 3.7051 | 70   | 12.2968       | -                      | -                      | -                      | -                      | -                     | -                     |
| 3.8136 | 72   | -             | 0.7690                 | 0.7559                 | 0.7467                 | 0.7276                 | 0.7120                | 0.6755                |


### 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
```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",
}
```

#### MatryoshkaLoss
```bibtex
@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
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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