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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:9432 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: Atherosclerosis and coronary heart disease are examples of what |
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type of body system disease? |
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sentences: |
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- Diseases of the cardiovascular system are common and may be life threatening. |
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Examples include atherosclerosis and coronary heart disease. A healthy lifestyle |
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can reduce the risk of such diseases developing. This includes avoiding smoking, |
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getting regular physical activity, and maintaining a healthy percent of body fat. |
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- Osmosis Osmosis is the diffusion of water through a semipermeable membrane according |
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to the concentration gradient of water across the membrane. Whereas diffusion |
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transports material across membranes and within cells, osmosis transports only |
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water across a membrane and the membrane limits the diffusion of solutes in the |
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water. Osmosis is a special case of diffusion. Water, like other substances, moves |
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from an area of higher concentration to one of lower concentration. Imagine a |
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beaker with a semipermeable membrane, separating the two sides or halves (Figure |
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3.21). On both sides of the membrane, the water level is the same, but there are |
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different concentrations on each side of a dissolved substance, or solute, that |
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cannot cross the membrane. If the volume of the water is the same, but the concentrations |
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of solute are different, then there are also different concentrations of water, |
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the solvent, on either side of the membrane. |
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- Circadian rhythms are regular changes in biology or behavior that occur in a 24-hour |
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cycle. In humans, for example, blood pressure and body temperature change in a |
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regular way throughout each 24-hour day. Animals may eat and drink at certain |
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times of day as well. Humans have daily cycles of behavior, too. Most people start |
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to get sleepy after dark and have a hard time sleeping when it is light outside. |
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In many species, including humans, circadian rhythms are controlled by a tiny |
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structure called the biological clock . This structure is located in a gland at |
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the base of the brain. The biological clock sends signals to the body. The signals |
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cause regular changes in behavior and body processes. The amount of light entering |
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the eyes helps control the biological clock. The clock causes changes that repeat |
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every 24 hours. |
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- source_sentence: How does a cell's membrane keep extracellular materials from mixing |
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with it's internal components? |
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sentences: |
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- We know that the Universe is expanding. Astronomers have wondered if it is expanding |
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fast enough to escape the pull of gravity. Would the Universe just expand forever? |
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If it could not escape the pull of gravity, would it someday start to contract? |
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This means it would eventually get squeezed together in a big crunch. This is |
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the opposite of the Big Bang. |
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- Physical properties that do not depend on the amount of substance present are |
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called intensive properties . Intensive properties do not change with changes |
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of size, shape, or scale. Examples of intensive properties are as follows in the |
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Table below . |
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- CHAPTER REVIEW 3.1 The Cell Membrane The cell membrane provides a barrier around |
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the cell, separating its internal components from the extracellular environment. |
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It is composed of a phospholipid bilayer, with hydrophobic internal lipid “tails” |
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and hydrophilic external phosphate “heads. ” Various membrane proteins are scattered |
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throughout the bilayer, both inserted within it and attached to it peripherally. |
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The cell membrane is selectively permeable, allowing only a limited number of |
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materials to diffuse through its lipid bilayer. All materials that cross the membrane |
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do so using passive (non energy-requiring) or active (energy-requiring) transport |
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processes. During passive transport, materials move by simple diffusion or by |
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facilitated diffusion through the membrane, down their concentration gradient. |
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Water passes through the membrane in a diffusion process called osmosis. During |
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active transport, energy is expended to assist material movement across the membrane |
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in a direction against their concentration gradient. Active transport may take |
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place with the help of protein pumps or through the use of vesicles. |
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- source_sentence: An infection may be intracellular or extracellular, depending on |
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this? |
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sentences: |
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- '22.3 Magnetic Fields and Magnetic Field Lines • Magnetic fields can be pictorially |
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represented by magnetic field lines, the properties of which are as follows: 1. |
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The field is tangent to the magnetic field line. Field strength is proportional |
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to the line density. Field lines cannot cross. Field lines are continuous loops.' |
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- Figure 24.13 The lifecycle of an ascomycete is characterized by the production |
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of asci during the sexual phase. The haploid phase is the predominant phase of |
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the life cycle. |
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- Caffeine is an example of a psychoactive drug. It is found in coffee and many |
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other products (see Table below ). Caffeine is a central nervous system stimulant |
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. Like other stimulant drugs, it makes you feel more awake and alert. Other psychoactive |
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drugs include alcohol, nicotine, and marijuana. Each has a different effect on |
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the central nervous system. Alcohol, for example, is a depressant . It has the |
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opposite effects of a stimulant like caffeine. |
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- source_sentence: What does water treatment do to water? |
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sentences: |
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- Some solutes, such as sodium acetate, do not recrystallize easily. Suppose an |
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exactly saturated solution of sodium acetate is prepared at 50°C. As it cools |
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back to room temperature, no crystals appear in the solution, even though the |
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solubility of sodium acetate is lower at room temperature. A supersaturated solution |
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is a solution that contains more than the maximum amount of solute that is capable |
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of being dissolved at a given temperature. The recrystallization of the excess |
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dissolved solute in a supersaturated solution can be initiated by the addition |
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of a tiny crystal of solute, called a seed crystal. The seed crystal provides |
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a nucleation site on which the excess dissolved crystals can begin to grow. Recrystallization |
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from a supersaturated solution is typically very fast. |
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- Figure 23.13, the esophagus runs a mainly straight route through the mediastinum |
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of the thorax. To enter the abdomen, the esophagus penetrates the diaphragm through |
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an opening called the esophageal hiatus. |
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- Water treatment is a series of processes that remove unwanted substances from |
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water. More processes are needed to purify water for drinking than for other uses. |
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- source_sentence: 'There are only four possible bases that make up each dna nucleotide: |
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adenine, guanine, thymine, and?' |
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sentences: |
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- Metamorphism. This long word means “to change form. “ A rock undergoes metamorphism |
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if it is exposed to extreme heat and pressure within the crust. With metamorphism |
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, the rock does not melt all the way. The rock changes due to heat and pressure. |
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A metamorphic rock may have a new mineral composition and/or texture. |
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- Forest and Kim Starr (Flickr:Starr Environmental). Secondary succession occurs |
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when nature reclaims areas formerly occupied by life . CC BY 2.0. |
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- 'The only difference between each nucleotide is the identity of the base. There |
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are only four possible bases that make up each DNA nucleotide: adenine (A), guanine |
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(G), thymine (T), and cytosine (C).' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: MNLP M3 Encoder SciQA |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 384 |
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type: dim_384 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6120114394661582 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8017159199237369 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8541468064823642 |
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|
name: Cosine Accuracy@5 |
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|
- type: cosine_accuracy@10 |
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value: 0.9275500476644424 |
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|
name: Cosine Accuracy@10 |
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|
- type: cosine_precision@1 |
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value: 0.6120114394661582 |
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|
name: Cosine Precision@1 |
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|
- type: cosine_precision@3 |
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value: 0.267238639974579 |
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|
name: Cosine Precision@3 |
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|
- type: cosine_precision@5 |
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value: 0.17082936129647283 |
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|
name: Cosine Precision@5 |
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|
- type: cosine_precision@10 |
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value: 0.09275500476644424 |
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name: Cosine Precision@10 |
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|
- type: cosine_recall@1 |
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value: 0.6120114394661582 |
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|
name: Cosine Recall@1 |
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|
- type: cosine_recall@3 |
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value: 0.8017159199237369 |
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|
name: Cosine Recall@3 |
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|
- type: cosine_recall@5 |
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value: 0.8541468064823642 |
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|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9275500476644424 |
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|
name: Cosine Recall@10 |
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|
- type: cosine_ndcg@10 |
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value: 0.7690377395004954 |
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|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7184669450875366 |
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|
name: Cosine Mrr@10 |
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|
- type: cosine_map@100 |
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value: 0.7210073638258574 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5977121067683508 |
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name: Cosine Accuracy@1 |
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|
- type: cosine_accuracy@3 |
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value: 0.7912297426120114 |
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|
name: Cosine Accuracy@3 |
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|
- type: cosine_accuracy@5 |
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value: 0.8398474737845567 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9151572926596759 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5977121067683508 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.26374324753733713 |
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name: Cosine Precision@3 |
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|
- type: cosine_precision@5 |
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value: 0.16796949475691134 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
|
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value: 0.09151572926596759 |
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name: Cosine Precision@10 |
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|
- type: cosine_recall@1 |
|
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value: 0.5977121067683508 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
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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: |
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type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
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name: dim 192 |
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type: dim_192 |
|
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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: |
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type: information-retrieval |
|
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name: Information Retrieval |
|
|
dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
|
|
- type: cosine_accuracy@1 |
|
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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: |
|
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name: dim 64 |
|
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type: dim_64 |
|
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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 |
|
|
--- |
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|
|
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|
# MNLP M3 Encoder SciQA |
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|
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|
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. |
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- json |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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|
(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'There are only four possible bases that make up each dna nucleotide: adenine, guanine, thymine, and?', |
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'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).', |
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'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.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
|
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Datasets: `dim_384`, `dim_256`, `dim_192`, `dim_128`, `dim_96` and `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | dim_384 | dim_256 | dim_192 | dim_128 | dim_96 | dim_64 | |
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|:--------------------|:----------|:-----------|:-----------|:-----------|:----------|:-----------| |
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| cosine_accuracy@1 | 0.612 | 0.5977 | 0.5891 | 0.5663 | 0.552 | 0.5167 | |
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| cosine_accuracy@3 | 0.8017 | 0.7912 | 0.7788 | 0.7626 | 0.7417 | 0.7045 | |
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| cosine_accuracy@5 | 0.8541 | 0.8398 | 0.8332 | 0.8265 | 0.8093 | 0.7684 | |
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| cosine_accuracy@10 | 0.9276 | 0.9152 | 0.9037 | 0.8913 | 0.8732 | 0.837 | |
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| cosine_precision@1 | 0.612 | 0.5977 | 0.5891 | 0.5663 | 0.552 | 0.5167 | |
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| cosine_precision@3 | 0.2672 | 0.2637 | 0.2596 | 0.2542 | 0.2472 | 0.2348 | |
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| cosine_precision@5 | 0.1708 | 0.168 | 0.1666 | 0.1653 | 0.1619 | 0.1537 | |
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| cosine_precision@10 | 0.0928 | 0.0915 | 0.0904 | 0.0891 | 0.0873 | 0.0837 | |
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| cosine_recall@1 | 0.612 | 0.5977 | 0.5891 | 0.5663 | 0.552 | 0.5167 | |
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| cosine_recall@3 | 0.8017 | 0.7912 | 0.7788 | 0.7626 | 0.7417 | 0.7045 | |
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| cosine_recall@5 | 0.8541 | 0.8398 | 0.8332 | 0.8265 | 0.8093 | 0.7684 | |
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| cosine_recall@10 | 0.9276 | 0.9152 | 0.9037 | 0.8913 | 0.8732 | 0.837 | |
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| **cosine_ndcg@10** | **0.769** | **0.7559** | **0.7467** | **0.7276** | **0.712** | **0.6755** | |
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| cosine_mrr@10 | 0.7185 | 0.705 | 0.6965 | 0.6752 | 0.6603 | 0.6239 | |
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| cosine_map@100 | 0.721 | 0.7085 | 0.7004 | 0.6794 | 0.6649 | 0.6293 | |
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<!-- |
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|
## Bias, Risks and Limitations |
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|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
|
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## Training Details |
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|
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### Training Dataset |
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#### json |
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* Dataset: json |
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* Size: 9,432 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
|
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | |
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| 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> | |
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* Samples: |
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| anchor | positive | |
|
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|:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
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| <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> | |
|
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| <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> | |
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| <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 |
|
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{ |
|
|
"loss": "MultipleNegativesRankingLoss", |
|
|
"matryoshka_dims": [ |
|
|
384, |
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256, |
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192, |
|
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128, |
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96, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
|
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1, |
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1, |
|
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1, |
|
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1 |
|
|
], |
|
|
"n_dims_per_step": -1 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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|
- `lr_scheduler_type`: cosine |
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|
- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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|
- `tf32`: True |
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|
- `load_best_model_at_end`: True |
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|
- `optim`: adamw_torch_fused |
|
|
- `batch_sampler`: no_duplicates |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
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|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: epoch |
|
|
- `prediction_loss_only`: True |
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|
- `per_device_train_batch_size`: 32 |
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|
- `per_device_eval_batch_size`: 16 |
|
|
- `per_gpu_train_batch_size`: None |
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|
- `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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